Method for Evaluating Risk in Multiple Sclerosis

ABSTRACT

The invention relates to methods and reagents for diagnosing and assessing the prognosis of multiple sclerosis. The present invention is based, in part, upon the discovery that anti-glycan antibodies are useful in evaluating the risk of whether clinically isolated syndrome (CIS) patients suggestive of Multiple sclerosis (MS) will have a clinical relapse within, e.g., 24 months. The invention is also based upon the discovery that anti-glycan antibodies are useful for evaluating the risk of CIS patients suggestive of MS to have a rapid disease progression and accumulate disabilities, e.g., permanent disability, within a certain time frame, e.g., 5 years.

RELATED APPLICATIONS

This application claims the benefit of U.S. Ser. No. 61/152,168, filed Feb. 12, 2009, the contents of which are incorporated by reference herein in their entirety.

FIELD OF THE INVENTION

The invention relates to methods and reagents for diagnosing and assessing the prognosis of multiple sclerosis.

BACKGROUND OF THE INVENTION

Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease of the central nervous system. It is a common cause of persistent disability in young adults. In patients suffering from MS, the immune system destroys the myelin sheath of axons in the brain and the spinal cord, causing a variety of neurological pathologies. In the most common form of MS, Relapsing-Remitting (RRMS), episodes of acute worsening of neurological function (exacerbations, attacks) are followed by partial or complete recovery periods (remissions) that are free of disease progression (stable).

There is a clinical need for a simple serological assay that predicts: i) whether patients at First Presentation (FP), also known as clinically isolated syndrome (CIS) suggestive of MS, will develop MS in a certain timeframe; ii) whether patients at CIS have a risk of rapid disease progression with accumulation of clinical disabilities and therefore might require aggressive disease modifying therapy (DMT); or iii) whether patients at CIS will have a regular or relatively slow MS disease progression with fewer disabilities that would require less aggressive therapy.

SUMMARY OF THE INVENTION

The invention solves a long standing problem in the field of MS—how to predict the disease course for a subject at clinically isolated syndrome (CIS) suggestive of MS. The method involves characterizing an individual as a member of one of two groups: (i) those who are likely to progress to clinically definite multiple sclerosis (CDMS) rapidly, i.e., within 24 months, and (ii) those who are likely to progress to CDMS more slowly, i.e., within greater than 24 months. The method also involves characterizing an individual as a member of one of two other groups: (i) those who are likely to progress to clinical disabilities rapidly, i.e., within 5 years, and (ii) those who are likely to progress to clinical disabilities more slowly, i.e., within greater than 5 years.

The present invention is based, in part, upon the discovery that anti-glycan antibodies are useful in evaluating the risk of whether clinically isolated syndrome (CIS) patients suggestive of Multiple sclerosis (MS) will have a clinical relapse within, e.g., 24 months. The invention is also based upon the discovery that anti-glycan antibodies are useful for evaluating the risk of CIS patients suggestive of MS to have a rapid disease progression and accumulate disabilities, e.g., permanent disability, within a certain time frame, e.g., 5 years.

The anti-glycan antibodies described herein are used to predict a second neurological attack within a defined time period, e.g., within about forty-eight months; within about thirty-six months within about twenty-four months; within about twelve months; or within about six months. The anti-glycan antibodies are also used to predict the progression in MS disease course as measured by the expanded disability status scale (EDSS), a rating system that is used for classifying and standardizing the condition of MS patients. For example, MS disease progression is predicted within a defined time period, e.g., within about twenty years; within about ten years; within about seven years; within about five years; or within about four years. MS disease progression is measured by EDSS units, e.g., progression is greater than one EDSS unit; progression is greater than two EDSS units; progression is greater than three EDSS units; or progression is greater than six EDSS units. The rate (fast vs. slow) of disease progression is determined by the relative amount/level of anti-glycan antibodies in a patient sample.

First, a test sample is provided from a CIS or newly diagnosed subject characterized as having suffered a first neurological event. Suitable test samples include a biological fluid selected from the group consisting of whole blood, serum, and plasma. Various anti-glycan antibodies are detected in the test sample, such as an anti-Glc (α 1,2) Glc (α) antibody (GAGA2), an anti-Glc (α 1,3) Glc (α) antibody (GAGA3), an anti-Glc (α 1,4) Glc (α) antibody (GAGA4), and an anti-Glc (α 1,6) Glc (α) antibody (GAGA6). Preferably, the antibodies are IgM type antibodies. The levels of the antibodies in the test sample are compared to the levels of the antibodies in a control sample, wherein a higher level of the antibodies in the test sample compared to the level of the antibodies in a control sample indicates the subject is at risk of having a second neurological attack within a defined time period, e.g., within about forty-eight months; within about thirty-six months within about twenty-four months; within about twelve months; or within about six months.

The control in relation to the prediction of a next relapse within 24 months is a reference value from one or more MS patients characterized as having suffered a first neurological attack followed by a subsequent (second) neurological attack more than twenty-four months after the first neurological attack. For example, the control reference level is a value associated with late relapse. Late relapse refers to an MS patient that has experienced a first neurological attack or first presentation followed by a second neurological attack twenty-four months or more after the first neurological attack or first presentation.

The invention also provides a method of differentiating between a CIS subject who will progress in Expanded Disability Status Scale (EDSS) score within twenty years of the subject's First Presentation (FP) and a subject who will not progress in EDSS score within twenty years of said subject's FP. First, a test sample is provided from a CIS or newly diagnosed subject characterized as having suffered a first neurological event. Suitable test samples include a biological fluid selected from the group consisting of whole blood, serum, and plasma. Various anti-glycan antibodies are detected in the test sample, such as an anti-Glc (α 1,2) Glc (α) antibody (GAGA2), an anti-Glc (α 1,3) Glc (α) antibody (GAGA3), an anti-Glc (α 1,4) Glc (α) antibody (GAGA4), and an anti-Glc (α 1,6) Glc (α) antibody (GAGA6). Preferably, the antibodies are IgM type antibodies. The levels of the antibodies in the test sample are compared to the levels of the antibodies in a control sample, wherein a higher level of the antibodies in the test sample compared to the level of the antibodies in a control sample indicates the subject is at risk of having rapid MS disease progression (severe disease course) with higher level of disabilities as measured by EDSS score within a defined time period, e.g., within about twenty years; within about ten years; within about seven years; within about five years; or within about four years. The method provides the clinician with valuable information to predict rapid or slow progression to permanent or clinical disability as measured by EDSS, for example, the subject progresses at least one EDSS unit, e.g., at least two EDSS units; at least three EDSS units; greater than three EDSS units; greater than four EDSS units; or greater than six EDSS units, etc., within twenty years. For example, rapid progression is characterized by advancement of 6 or more EDSS units within 20 years. Slow progression is characterized by advancement of 3 or fewer EDSS units within 20 years.

Alternatively, a low level of the antibodies in the test sample equal or similar to the level of the antibodies in a control sample indicates the subject is likely to have a slow MS disease progression within same time period. The control in relation to the prediction of rapid MS disease progression is a reference value from one or more MS patients characterized as having higher or lower EDSS score.

By “reference level” is meant the mean, median, or range level of each antibody in a defined cohort of MS individuals. For example, the cohort is characterized by patients who progressed from CIS to next relapse in 24 months or who experienced rapid progression of MS disability within a defined time period. An exemplary “reference level” of antibody is the mean+/−0.5, 1, 1.5, or 2 standard deviations (SD) of the mean. Preferably, the reference level is the mean antibody level+/−1.5 SD. The population of individuals is at least 5; at least 10, at least 25; at least 50; at least 100 or more individuals. Reference levels are obtained using samples from patients with a known MS progression profile, e.g., archived blood, serum, or plasma samples.

In practice, by examining a statistically-relevant cohort of patients (e.g., 50 patients, 100 patients, etc.) a reference sera sample similar to the chosen cutoff level is determined for each antibody, i.e., the upper 25%, the upper 20%, the upper 15%, the upper 10%; or the lower 25%, the lower 20%, the lower 15%, or the lower 10%. Antibody levels in the upper percentiles indicate a risk of rapid relapse or rapid disease progression, while antibody levels in the lower percentiles indicate a slow disease progression and low risk of relapse. Aliquots of the reference sera samples are prepared and kept frozen for future use. The reference sera samples are calibration samples, having a preset antibody level (cutoff level; reference level; control level). When test samples are analyzed for their antibody levels, the test sample is examined in parallel with the reference/calibration/control sample. If the antibody level in the test sample is higher compared to the antibody level in the reference/calibration sample, the test sample is considered “positive”, e.g., it belongs to the subgroup of CIS patients with an elevated level of the antibody. Similarly, if the antibody level in the test sample is equal/similar or lower compared to the antibody level in the reference/calibration sample, the test sample is considered “negative”, e.g., it belongs to the subgroup of CIS patients without an elevated level of the antibody.

The clinical parameters of sensitivity, specificity, negative predictive value, positive predictive value and efficiency are calculated using true positives, false positives, false negatives and true negatives. A “true positive” sample is a sample positive for MS according to art-recognized methods for diagnosing/prognosing MS, which is also diagnosed positive according to a method of the invention. A “false positive” sample is a sample negative by an art-recognized method, which is diagnosed positive according to a method of the invention. Similarly, a “false negative” is a sample positive for an art-recognized analysis, which is diagnosed negative according to a method of the invention. A “true negative” is a sample negative for the assessed trait by an art-recognized method, and also negative according to a method of the invention. See, for example, Mousy (Ed.), Intuitive Biostatistics New York: Oxford University Press (1995), which is incorporated herein by reference.

As used herein, the term “sensitivity” means the probability that a laboratory method is positive in the presence of the measured trait. Sensitivity is calculated as the number of true positive results divided by the sum of the true positives and false negatives. Sensitivity essentially is a measure of how well a method correctly identifies those with the clinical question. In a method of the invention, the antibody values can be selected such that the sensitivity of diagnosing/prognosing an individual whether with higher risk for next relapse or rapid MS progression is at least about 20%, and can be, for example, at least about 30%, 40%, 50%, 60%, 70%, 80% or 90%.

As used herein, the term “specificity” means the probability that a method is negative in the absence of the measured trait. Specificity is calculated as the number of true negative results divided by the sum of the true negatives and false positives. Specificity essentially is a measure of how well a method excludes those who do not have the measured trait. The antibody cut-off value can be selected such that, when the sensitivity is at least about 30%, the specificity of diagnosing/prognosing an individual is in the range of 60-70%, 80-85%, 90-95%, or 95-99.9%.

By “progression in EDSS score” is meant a progression from about 1.0 to about 10.0 in increments of 0.5 or 1.0, e.g., about 1.0 to about 5.0; about 5.0 to about 10.0; about 2.0 to about 4.0; about 4.0 to about 6.0; about 6.0 to about 8.0; about 8.0 to about 10.0; about 1.5 to about 2.5; about 3.0 to about 3.5, or any other incremental increase between 1.0 and 10.0.

In some instances, MS patients have a poor prognosis despite treatment. The term “breakthrough disease” is used to indicate clinical or imaging evidence for disease activity or progression (e.g., measured by Expanded Disability Status Scale (EDSS score)) despite disease-modifying therapy (DMT). Breakthrough disease does not imply that the patient has not responded to treatment—it implies only that disease activity is present despite therapy. Prior to the invention, it was difficult to differentiate between a rapid progression good responder to DMT and a slow progressor with a poor response to DMT. Accordingly, the invention provides methods of identifying a subject who will develop breakthrough MS. First, a test sample is provided from a subject. Suitable test samples include a biological fluid selected from the group consisting of whole blood, serum, and plasma. An antibody selected from the group consisting of anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody is detected in the test sample. In one aspect, the antibody is an IgM antibody. The levels of the antibodies in the test sample are compared to a control level of the antibodies, wherein a higher level of at least one of the antibodies compared to the control level of the antibodies indicates that the subject is likely to develop breakthrough MS.

Alternatively, the levels of the antibody in the blood sample are compared to levels of the antibody in a control sample and, a subject who will not progress to a breakthrough MS is differentiated by detection of an equal/similar or lower level of the antibody in the subject sample compared to levels of the antibody in the control sample.

The invention also provides methods of identifying a subject with a clinically isolated syndrome (CIS) who will progress to clinically definitive multiple sclerosis (CDMS) within twenty-four months by providing a blood sample from the subject and detecting in the blood sample an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody. The levels of the antibodies in the blood sample are compared to levels of the antibodies in CIS individuals who did not progress to CDMS within twenty-four months. A subject with CIS who will progress to CDMS within twenty-four months is identified by detection of higher level of the antibodies in the blood sample compared to levels of the antibodies in CIS individuals who did not progress to CDMS within twenty-four months.

The invention also provides methods of identifying a subject with CIS who is likely to experience rapid progression of MS disease severity within twenty years by providing a blood sample from a subject at the time of a CIS and detecting in the blood sample an anti-Glc(α 1,2) Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, and an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody. The levels of the antibodies in the blood sample are compared to levels of the antibodies in individuals who did not experience rapid progression of MS disease severity within twenty years. A subject with CIS who is likely to progress greater than one unit in a EDSS score within twenty years is identified by detection of higher level of the antibodies in the blood sample compared to levels of the antibodies in individuals who did not experience rapid progression of MS disease severity within twenty years, the EDSS score being indicative of disease severity.

The invention also provides methods of identifying a subject with CIS who is likely to experience a slow progression of MS disease severity by providing a blood sample from a subject at the time of a CIS and detecting in the blood sample an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, and an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody. The levels of the antibodies in the blood sample are compared to levels of the antibodies in individuals who experienced a slow progression of MS disease severity within twenty years. A subject with CIS who is likely to progress slowly in EDSS score within twenty years is identified by detection of an equal/similar or lower level of the antibodies in the blood sample compared to levels of the antibody in individuals who experienced a slow progression of MS disease severity within twenty years, the EDSS score being indicative of disease severity.

The invention also provides methods of identifying a subject who will develop breakthrough MS by providing a blood sample from the subject, and detecting in the blood sample an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (a 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody. The levels of the antibodies in the blood sample are compared to levels of the antibodies in individuals that did not develop breakthrough MS, e.g., individuals that do not progress by clinical or image detection with or without DMT treatment. A subject who will develop breakthrough MS is identified by detection of higher level of the antibody in the blood sample compared to levels of the antibody in individuals that did not develop breakthrough MS.

The invention also provides a computer-readable medium having computer-executable instructions for performing a method. First, at least one first variable associated with the level of at least one antibody in a blood sample of a patient is stored. Optionally, the antibody levels are detected via binding to immobilized glycans in a chamber or well, and emitting a fluorescent signal; chemiluminescence signal; or optical density signal by adding a pre-labeled secondary antibody recognizing a common reign in IgM isotype. Second, at least one second variable associated with at least one reference level is stored. The patient's risk factor for the relevant clinical question: i) conversion to clinically definite multiple sclerosis (CDMS); ii) rapid progression in MS as measured by EDSS; or iii) breakthrough MS, is calculated as a function of at least the first and second variables. Finally, the risk factor is outputted. Optionally, the at least one first variable corresponds to levels of an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody in the blood sample. In another aspect, the at least one first variable corresponds to the level of said antibodies in the blood selected from the group consisting of: an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, and an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In the case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

Other features and advantages of the invention will be apparent from the following detailed description and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a glycan array format: A) glass slide patterned with Teflon mask creating 7 clusters of microwells, 32 wells in each cluster; B) an adhesive silicon superstructure attached to the slide defines wells for manual application of multiple serum samples per slide; and C) antigens and controls lay out in each gasket well.

FIG. 2 shows a time to clinically definite MS (CDMS), Kaplan-Meier, survival plot for cohort-C. FP patients positive for ≧1 (anti-GAGA4, anti-GAGA2, anti-GAGA3, or anti-GAGA6 IgM) versus patients negative for all markers. Cutoff values: 4.0, 4.5, 4.5, and 4.3 for anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6 μM, respectively. P-values determined by log rank test. With Bonferroni correction only P-values below 0.0125 are considered significant.

FIG. 3 shows a time to confirmed EDSS progression, Kaplan-Meier, survival plot. CIS patients positive for ≧1 (anti-GAGA2, anti-GAGA3, anti-GAGA4, or anti-GAGA6 IgM) versus patients negative for all markers. Cutoff values: 148.8, 164.6, 133.6, and 168,1 EU for anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6 IgM, respectively. P-value determined by log rank test was P=0.011664.

FIG. 4 shows a block flow diagram of a process of determining whether a CIS patient is at risk of a relapse within 24 months.

FIG. 5 shows a block flow diagram of a process of determining whether a patient is likely to progress in Expanded Disability Status Scale (EDSS) score within twenty years.

FIG. 6 shows a block flow diagram of a process of determining whether a patient is likely to develop breakthrough MS.

DETAILED DESCRIPTION

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS), though the exact etiology and pathogenesis have not yet been deciphered. Diagnosis of MS requires exclusion of diseases that could better explain the clinical, imaging, and paraclinical findings. The MS diagnosis criteria, also known as the revised McDonald criteria, define the parameters required for a subject having MS (Polman et al., 2005 Ann Neurol, 58: 840-6). To better define suspected MS cases that may or may not meet the McDonald criteria, an International Panel of MS experts recently developed consensus-determined guidelines for differential diagnosis leading to MS (Miller et al., 2008 Multiple Sclerosis, 14:1157-1174).

The course of MS disease follows unpredictable patterns of evolution and widely variable timetables, with disability accumulation adhering to no particular blueprint making it difficult or impossible to accurately determine the prognosis of individual patients (Vollmer T, 2007 J Neurol Sci, 256 Suppl 1:S5-13). Heterogeneity can be related to the timeframe a CIS patient will have the next relapse or the response to disease modifying therapy (DMT). Heterogeneity can also refer to the disease progression rate and accumulation of disabilities. In many instances, MS patients have a poor prognosis despite treatment. Specifically, the term “breakthrough disease” is used to indicate clinical or imaging evidence for disease activity or progression (as may measured by Expanded Disability Status Scale (EDSS score)) despite DMT (Rudick and Polman 2009, Lancet Neurology, 8:545-559). Breakthrough disease does not imply that the patient has not responded to treatment—it implies only that disease activity is present despite therapy.

The invention describes methods regarding the prediction of disease course for a subject at clinically isolated syndrome (CIS) suggestive of MS. The method involves characterizing an individual as a member of one of two groups: (i) those who are likely to progress to clinically definite multiple sclerosis (CDMS) rapidly, i.e., within 24 months, and (ii) those who are likely to progress to CDMS slower, i.e., within greater than 24 months. The method also involves characterizing an individual as a member of one of two other groups: (i) those who are likely to progress in clinical disabilities rapidly, i.e., within 5 years, and (ii) those who are likely to progress in clinical disabilities more slowly, i.e., within greater than 5 years.

The current clinical practice is to treat CIS and newly diagnosed patients with first line DMT. Physicians point out the benefits of starting treatment early, but not all patients comply. A recent survey among United States neurologists revealed that at least 10% of newly diagnosed MS patients delayed their treatment due to their fear of injections (Perrin et al., 2009 JMCP, 15(7): 572). Therefore, the segregation of patients at CIS based on the risk to convert from CIS to CDMS within 24 months can be an important convincing tool for patient compliance with early DMT treatment, because those in group (i) will agree to receive DMT, whereas those in group (ii) can delay treatment until the next clinical or image episode.

Another aspect of current clinical practice is switching to another existing DMT due to the failure of the first DMT. Switching can be for another 1^(st) line DMT or to a more aggressive 2^(nd) line (2^(nd) generation) DMT. Therefore, the segregation of patients at CIS based on predicting the risk of rapid progression of MS and the risk of having more disabilities as measured by EDSS within, e.g., 5 years, is a critical factor in the determination of a treatment regimen, because those in group (i) will receive an aggressive DMT regimen, whereas those in group (ii) receive less aggressive DMT or another 1^(st) line DMT.

During symptomatic attacks, administration of high doses of intravenous corticosteroids, such as methylprednisolone is the routine therapy for acute relapses. The aim of this kind of treatment is to end the attack sooner and leave fewer lasting deficits in the patient. Although generally effective in the short term for relieving symptoms, corticosteroid treatments do not appear to have a significant impact on long-term recovery (Brusaferri and Candelise, 2000 J. Neurol., 247(6): 435-42). Potential side effects include osteoporosis and impaired memory, the latter being reversible (Dovio et al., 2004 Clin. Endocrinol. Metab., 89(10): 4923-8; Uttner et al., 2005 Neurology, 64(11): 1971-3). Severe attacks which do not respond to corticosteroids might be treated by plasmapheresis. The earliest clinical presentation of relapsing-remitting MS (RRMS) is the clinically isolated syndrome (CIS). Several studies have shown that treatment with interferons during an initial attack can decrease the chance that a patient will develop clinical MS (Jacobs et al., 2000 N Engl J Med, 343(13): 898-904; Comi et al., 2001 Lancet, 357(9268): 1576-82; Kappos et al., 2007 Lancet, 370(9585): 389-97). These and other CIS studies that showed the benefit in early treatment with DMT evolved to current clinical practice in which newly diagnosed RRMS and CIS suggestive of MS patients are initiating DMT treatment immediately after first presentation and abnormal MRI scan.

Six disease-modifying treatments have been approved by regulatory agencies of different countries for RRMS (Rudick and Polman 2009, Lancet Neural, 8: 545-559). Three are interferons: two formulations of interferon beta-1a (trade names Avonex®, CinnoVex™, ReciGen and Rebif®) and one of interferon beta-1b (U.S. trade name Betaseron®, in Europe and Japan Betaferon®). A fourth medication is glatiramer acetate (Copaxone®). The fifth medication, mitoxantrone, is an immunosuppressant also used in cancer chemotherapy, approved only in the USA and largely for secondary progressive MS. The sixth is natalizumab (marketed as Tysabri®). All six medications are modestly effective at decreasing the number of attacks and slowing progression to disability, although their efficacy rates differ, and studies of their long-term effects are still lacking (Ruggieri et al., 2007 CNS Drug Rev, 13(2): 178-91; Munari et al., Cochrane Database of Systematic Reviews 2003, Issue 4. Art. No.: CD004678; Rice et al., 2001 Cochrane Database Syst Rev (4): CD002002; Martinelli Boneschi et al., 2005 Cochrane Database Syst Rev (4): CD002127). Comparisons between immunomodulators (all but mitoxantrone) show that the most effective is natalizumab, both in terms of relapse rate reduction and halting disability progression (Johnson K P, 2007 J. Neurol. Sci., 256 Suppl 1: S23-8); it has also been shown to reduce the severity of MS. Mitoxantrone may be the most effective of them all (Gonsette R E, 2007 Expert opinion on pharmacotherapy, 8(8): 1103-16); however, it is generally not considered as a long-term therapy, as its use is limited by severe cardiotoxicity (Murray T J, 2006 Expert opinion on drug safety, 5 (2): 265-74). The interferons and glatiramer acetate are delivered by frequent injections, varying from once-per-day for glatiramer acetate to once-per-week (but intra-muscular) for Avonex®. Natalizumab and mitoxantrone are given by IV infusion at monthly intervals. These DMTs are also known as 1^(st) line DMT.

Recently, a “true” 2^(nd) generation DMT successfully completed phase III clinical trials. Fingolimod (FTY720) and Mylinax (Cladrabine) have shown higher efficacy as compared to the 1^(st) line DMT studies in the active arm during these phase III clinical trials. 2nd generation Disease Modifying Therapy (DMT) is currently being introduced to the MS market; however, adverse side affects such as death from Herpes infections and skin cancers were reported during the phase III clinical studies. Due to the severe side effects of the 2^(nd) generation DMTs, they will not be prescribed to all patients. In addition, four other agents with more active efficacy than 1^(st) line DMTs, but with adverse side effects, are currently in phase III clinical trials.

The heterogeneity of disability progression in MS patients is seen by observing the Expanded Disability Status Scale (EDSS) in populations having an RRMS onset at different time intervals. After 5 years from disease onset, 10-15% of RRMS patients reached an expanded disability status score (EDSS) of 3.0 (corresponding to a patient who is fully ambulatory, but has moderate disability), after 8-12 years, 27%-30% of RRMS patients had an EDSS score of 3, and by 20 years, 39% of RRMS patients had an EDSS score of up to 3, while 39% of RRMS patients had an EDSS score of at least 6 (corresponding to a patient needing assistance of a cane to walk; Simone I L et al., 2002 Neurology, 24; 59(12): 1922-8; Pittock S J et al., 2004 Ann Neurol, 56: 303-306; Tintoré M et al., 2006 Neurology, 67: 968-972).

Recently, clinically isolated syndrome (CIS) was “re-defined” as a monophasic presentation with suspected underlying inflammatory demyelinating disease (Miller D H et al., 2008 Multiple Sclerosis, 14: 1157-1174). “Monophasic presentation” implies a single clinical episode at first presentation that is of relatively rapid onset. Multiple simultaneous clinical/paraclinical presentations (representing dissemination in space) are possible, although dissemination in time should not be evident. Four classes of CIS were defined based on whether the monophasic clinical presentation has mono- or multifocal clinical or MRI features (Miller D H et al., 2008 Multiple Sclerosis, 14:1157-1174). Finally, there should be reasonable grounds for suspecting inflammatory demyelinating disease as the underlying pathology.

In CIS patients suggestive of multiple sclerosis, the EDSS progression is as follows. Twenty-five percent (25%) of CIS patients have an EDSS score of at least 6.0 after 20 years. Approximately 45% of CIS patients reach an EDSS score of 3.0 during the same 20 year period, while the remaining 30% of CIS patients have an EDSS score under 3.0 after 20 years (Fisniku L K et al., 2008 Brain, 131: 808-817). Thus, a certain subpopulation of patients with RRMS onset have a more severe disease, in that these patients have a higher probability of developing a greater disability level, i.e., rapid or “fast” MS patients. Certain clinical (e.g., male gender, late age of onset, and polysymptomatic onset) and paraclinical factors (e.g., number, volume, and site of magnetic resonance Imaging (MRI) lesions, and presence of oligoclonal bands (OBs) in cerebrospinal fluid (CSF)) have shown a moderate ability to predict patients whose disability will progress more rapidly (Tintoré M et al., 2006 Neurology, 67:968-972; and Bergamaschi R 2007 Int Rev Neurobiol, 79:423-447). However, these predictive variables have limitations and only seem to aid in disability prediction of up to an EDSS score of 4 (Confavreux C et al., 2003 Brain, 126:770-782). As all current Disease Modifying Therapy (DMT) can only be utilized in patients with an EDSS score lower than 6.5, and as 2^(nd) generation aggressive DMT are associated with adverse side affect or are recently completed phase III studies, a set of prognostic tools that can improve the ability of the clinician to predict when a CIS patient will have a next relapse or if a CIS patient will progress quickly is needed. Such a prognostic tools will aid in the appropriate counsel of patients, identification of patients for more aggressive DMT, and improve the design and analysis of therapeutic trials (Miller D H, 2004 NeuroRx, 1:284-294; Bergamaschi R 2007 Int Rev Neurobiol, 79:423-447).

Prior to the invention, no known antigenic specificity profile existed to aid in the determination of disease activity (next relapse), disease severity (breakthrough disease), or the rate of disease course progression (EDSS progression). A test such as the one described herein that provides information regarding next relapse or how fast MS is likely to progress informs a physician to appropriately tailor therapeutic intervention. For example, a patient who is incorrectly considered to be unlikely to develop clinically definite MS (CDMS) within 24 months based upon the lack of an abnormal MRI scan at FP is treated if the methods of the invention indicates that the patient is likely to have a relapse or progress within 24 months. A patient who will imminently experience a CDMS neurological attack and who has abnormal MRI, but refuses DMT can be convinced to comply with therapy if the methods of the invention predict the CIS patient has a high likelihood for next relapse within 24 months.

Other examples of the clinical utilities of such a test and its contribution to current clinical practice is related to the ability of these four glycans to predict rapid MS progression and breakthrough disease. About 25% of CIS cases will progress to an EDSS score of 6.0 within 20 years. The remaining 75% will either progress to an EDSS score of 3.0 (45%) or will be below an EDSS score of 3.0 (Fisniku L K et al., 2008 Brain, 131: 808-817). The current 1^(st) line of DMT (when prescribed at CIS) postpones the next relapse on average for 2 years (Kappos L, et al., 2007 Lancet, 370(9585):389-97). An MS specialist will not know if the newly-diagnosed MS patient or CIS patient is a rapid or slow progressor, responder or poor responder to DMT, or the likelihood for a breakthrough disease. Therefore, most of newly diagnosed MS patients or CIS patients with abnormal MRI will be prescribed with 1^(st) line DMT. At day 731, a patient with an additional clinical event will still not have sufficient data regarding the course of disease. Such a patient might be a rapid MS progressor that respond well to DMT. Alternatively, the same patient could be a slow progressor patient that will not respond to DMT. The current practice is “wait and see”. As time lapses and more data on MRI lesion, and EDSS progression accumulates, a decision becomes possible. The four glycans which predict at CIS which patients have a high likelihood of progressing disability enables the decision of switching therapy to more aggressive treatment, if necessary. For a “positive” patient, the switching can be for the new 2^(nd) generation DMT, which are more aggressive and associated with adverse side affects. By contrast, a “negative” patient, can be offered less aggressive treatment, e.g., 1^(st) line DMT.

Currently, self-injectable and infusion therapies are the only approved treatments for relapsing forms of MS in the United States. Oral therapies like Fingolimod and Mylinax are in development and will soon expand the treatment options. Recently, in order to examine physician perspectives of the unmet needs in the treatment of MS related to CIS patients, an in-depth, telephonic survey was conducted among a sample of neurologists in the United States. The survey was fielded between November 2008 and February 2009. This survey, a joint collaboration between the National Multiple Sclerosis Society (NMSS) and EMD Serono, Inc., was conducted by GFK Roper. Among the neurologists participating in this survey (n=250), most were from private practice (77%) and had ≧16 years experience treating MS (57%). Almost all physicians (98%) indicated that they always explain the benefits of starting drug treatment early with their patients. Physicians state that, on average, at least 10% of their newly diagnosed MS patients in the past 12 months delayed treatment for at least 6 months. Physicians believe fear of injections (68%), fear of possible side effects (66%), and denial about having MS (61%) are the top reasons for the delay. When describing desired improvements in MS therapy, 70% specifically mentioned oral administration, followed by better balance of efficacy and safety (49%) and fewer side effects (47%). In ranking overall perceived benefits, physicians believe that oral therapies will help improve patient adherence (53% total mention), improve quality of life (49% total mention), and make it easier for patients to stay on treatment (47% total mention). When asked specifically about adherence, almost all physicians (97%) agree that an oral therapy, if approved, would improve the patient's ability to adhere to MS medication as prescribed. Physicians point out the benefits of starting treatment early, but not all patients comply. The availability of treatments that can be administered orally was the most desired improvement in MS therapy (Perrin et al., 2009 JMCP, 15(7): 572).

Thus, the panel of 4 glycans and the assay that measures blood levels of antibodies to those 4 glycans provide invaluable information upon which the treating physician relies to design the most effective treatment regimen, as well as provide a tool for convincing CIS patients suggestive of MS to be in treatment compliance as prescribed by their physicians.

The search has been ongoing for useful serum derived biomarkers, including antibodies. Serum IgM antibodies to an N-glucosylated peptide were specifically increased in relapsing remitting multiple sclerosis (RRMS) patients (Lolli F, et al. 2005 Proc Natl Acad Sci USA, 102: 10273-78; Lolli F, et al. 2005 J Neuroimmunol, 167: 131-37). High antibody titers to two myelin peptides, myelin oligodendrocyte glycoprotein (MOG) and myelin basic protein (MBP) were reported by some (Berger T, et al. 2003 N Engl J Med, 349: 139-45), but not others (Kuhle J, et al. 2007 N Engl J Med, 356: 371-78) to predict early relapse in CIS patients.

The finding of IgG antibody formation specifically in the cerebrospinal fluid (CSF), but not in a corresponding serum (i.e., oligoclonal banding), has been a useful test for diagnosis and differential diagnosis of MS (Freedman M S, et al. 2005 Arch Neurol, 62:865-70). Moreover, recent results show elevated levels of IgM antibodies to Glc (α1,4) Glc (α) (GAGA4) in RRMS patients in comparison to patients with other neurological diseases (OND) (Schwarz M, et al. 2006 J Neurol Sci, 244: 59-68). The results presented herein identify when during the course of disease (beginning at CIS) higher antibody levels to GAGA2, GAGA3, GAGA4 or GAGA6 predict various clinical episodes associate with disease activity (next relapse), disease severity (breakthrough), or its disease progression within a certain time frame.

Prior to the invention, there was no specific panel of serum based biomarkers known for the diagnosis or prognosis of relapsing-remitting multiple sclerosis (RRMS). As described below, cohort-C (FP (n=100)) IgM antibodies to glucose based glycan panel were measured by immunofluorescence. In cohort C, 58 patients experienced early relapse (<24 months); 31 had late relapse (≧24 months); and 11 did not experience second attack during follow-up. Kaplan-Meier curves demonstrated decrease in time to next relapse for patients positive for the antibody panel (P=0.02, log rank).

Example 1 Anti-α-Glucose Based Glycan IgM Antibodies Predict Relapse Activity in Multiple Sclerosis After the First Presentation Serum Samples

The results presented herein are from a retrospective study of frozen (−70° C.) and re-thawed serum samples collected from patients at the time of diagnostic work-up for their FP who were later diagnosed as RRMS. The control group included sera samples taken from patients with other neurological diseases (OND) that were stored around the same time from routine samples sent to the respective cerebrospinal fluid (CSF) diagnostic laboratories. Demographic and clinical data were obtained from hospital records. Inclusion criteria for MS samples were as follows: patient age (18-60 years) at time of sampling, follow-up for at least 4 years from blood sampling, and diagnosis of RRMS according to Poser criteria (Poser C M, et al. 1983 Ann Neurol, 13: 227-31). Samples meeting the above criteria were identified from one of two serum repositories located at the Ottawa Hospital-General Campus, Ottawa, Canada or the Cliniques Universitaires Saint-Luc in Brussels, Belgium. Relapse was defined as any new neurological event accompanied by symptoms or signs, or significant worsening of previous symptoms or signs in the absence of fever that lasted at least 48 hours. All samples were encoded at respective institutions before being sent to Glycominds Ltd. laboratories for antibody analysis. Decoding occurred only after all analyses was completed. Three distinct cohorts were analyzed: cohort-A included 88 samples (44 FP n=44, OND n=44), OND patients were matched to the MS patients according to age and gender; cohort-B included 252 samples (FP n=167, OND n=85); and cohort C included 100 FP patients. All samples were assayed in a ‘blinded’ fashion.

Total IgM Measurement

Total IgM levels were measured using a commercial enzyme linked immunosorbent assay (ELISA) kit (Bethyl laboratories, Montgomery, Tex., USA) according to manufacturer instructions and reported in mg/mL.

Immunofluorescence Assay for anti-GAGA2, anti-GAGA3, anti-GAGA4, and Anti-GAGA6 IgM Using Glycan Array

Levels of anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6 IgM antibodies in cohort C samples were measured by immunofluorescence assay (IFA), using glass slides patterned with teflon mask, creating 7 clusters of microwells, 32 wells in each cluster (FIG. 1A). An adhesive silicon superstructure (FIG. 1B) was attached to the slide. This silicon gasket defined wells for manual application of multiple serum samples per slide. Each well was arrayed with glycan antigens and internal controls (FIG. 1C). p-Nitrophenyl derivatives of GAGA2, GAGA3, GAGA4, and GAGA6 (Toronto Research Chemicals, Toronto, Canada) were covalently bound by a linker to the glass slide as previously described (Schwarz M, et al. 2006 J Neurol Sci, 244: 59-68).

Assay Procedures

The slide wells were incubated for 60 minutes at room temperature with blocking solution (400 μL/well). After removal of blocking solution, 300 μL/well of patients' sera, diluted 1:40 in aqueous solution of 1% bovine serum albumin (BSA) in 20 mM Tris-HCl pH 7.2, 0.9% NaCl, 0.05% Tween-20 was added to each well. Each slide included 5 sera samples and a reference sample for one arbitrary unit. Each sample was tested five times on different slides. Samples were incubated for 45 minutes. Sera were removed and slides further washed and processed in an HS4800 system. Briefly, slides were washed in TNTT buffer (20 mM Tris-HCl pH 7.2, 2 M NaCl, 0.05% Tween-20, 0.05% Triton X-100) by the hybridization system. Biotinylated goat anti-human IgM (1:500) and Alexa-633-labeled streptavidin (1:150; Molecular Probes, Oreg., USA) were incubated sequentially with washings in between for 1 h at 32° C. in the light-protected and temperature-controlled environment of the hybridization system.

Following the washing and drying, slides were scanned using laser scanner (GenePix 4000B, Molecular Device, Baltimore, Md., USA); slide image was analyzed using Optiquant™” software and RFU representing relative binding of anti-glycan IgM to each antigen and control micro-well were calculated. The data quality from each well was verified by ensuring signal levels from human IgM, and anti-human IgM spots above cutoff. If wells did not meet criteria, samples were tested again. Levels of anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6 IgM were calculated for each sample by dividing the sample RFU by the reference sample RFU. The coefficient of variation (CV) of anti-α-glucose levels was 8-12% for intra-slides wells, and 15-22% for different hybridization station running cycles (inter-slide). Samples were considered as positive if results were above cutoff levels for at least one of the 4 antibodies. Cutoff values for each antigen were calculated as mean value of the FP population plus 1, 1.5, or 2 SD, and best fit cutoffs (4.0, 4.5, 4.5, and 4.3 for anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6 IgM respectively).

Statistical Methods

Numerical variables were compared across groups by Student's t-test or by the Mann-Whitney U-test, depending on whether or not they followed a normal distribution, and the X² test for rates comparison between groups or the Fisher exact if any cells had an expected count of less than 5. Pearson correlations were calculated between numerical variables. P-values of less than 0.05 were considered to be statistically significant. Uncertainty of results was expressed by 95% confidence intervals. Diagnostic accuracy was calculated by sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). The cumulative risk of the development of clinically definite MS (CDMS) was calculated for each group according to the Kaplan-Meier method, and differences between the groups were evaluated in a univariate analysis by the log-rank test.

For testing the ability of the marker antibodies to differentiate FP patients with a high risk vs. a low risk for early conversion to CDMS (cohort C), several cutoff values were investigated. A Bonferroni correction factor was applied to the analysis.

Cohort-A: Identifying FP Patients Who Will have a Second Attack within 24 Months

Clinical and demographic characteristics as well as anti-GAGA4 levels for cohort-A are described in Table 1. Data regarding the time period between blood extraction and first relapse was available for 41/44 FP patients. Twenty six (26/41) FP patients (63%) had their first relapse within 2 years of blood sampling. Mean age of these patients was significantly lower than those suffering from a second attack later (mean age 34 versus 42 years, p=0.001); however, no association was found between age and anti-GAGA4 IgM levels among the study population. To examine for a relationship between anti-GAGA4 IgM levels and the risk of an earlier (i.e., 2 vs. 4 years) second attack, patients whose anti-GAGA4 IgM levels were above the median antibody levels for the FP group as a whole were examined. Sixteen (16/20) patients (80%) with antibody titers above median had a second clinical attack within two years compared to only 10/21 (47%) patients with titers equal or below the median (odds ratio 4.4 CI 95% 1.1-17.7, Fisher exact test, P=0.05).

TABLE 1 Demographic, clinical characteristics and anti-GAGA4 IgM levels of cohort-A. OND FP (total n = 44, Ottawa Brussels (n = 44) OIND 21, ONIND 23) (n = 66) (n = 22) Mean age, years (SD) 37.6 (9.0) 38.5 (9.5) 39.1 (8.8) 35.1 (9.8) Female, n (%) 38 (86) 33 (75) 55 (83)^(a) 16 (73) Center Ottawa, n (%) 37 (84)^(b) 26 (59) NA NA Total IgM, Mean RFU*10⁶ (SD) 2.10 (0.80) 1.94 (0.68) 2.08 (0.79) 1.83 (0.53) 2nd relapse within 24 months, 26 (60)^(c) NA 21 (57) 5 (71) n (% of FP population only) Anti-GAGA4 IgM, 0.44 (0.30)^(d) 0.30 (0.11) 0.37 (0.26) 0.35 (0.16) Mean signal intensity, OD/(Total IgM RFU*10⁶)^(0.5) (SD) All FP patients had CDMS at the end of the follow-up. Other inflammatory neurological disease (OIND) group included: 2 bacterial meningitis, 4 viral meningitis, 1 IVIg induced meningitis, 2 optic neuritis, 2 cerebral vasculitis, 1 Churg-Strauss vasculitis, 2 myelitis, 1 sarcoidosis, 1 SLE, 1 HIV, 1 sinusitis, 1 brachial plexitis, 1 epidural abscess; Other non-inflammatory neurological disease (ONIND) group included: 2 fibromyalgia, 2 migraine, 5 idiophathic headache, 1 progressive dementia, 1 motor neuron disease, 1 valproate induced encephalophaty, 1 migraine + stroke, 1 dementia NYD, 1 neurosis, 1 brainstem stroke, 1 anisocoria, 1 chronic insomnia, 1 diabetic amyotropy, 1 numbness, 1 motor neuropathy, 1 occipital neuralgia and ataxia, 1 breast cancer: NA, not applicable. [Note: all the data in this Table derive from samples collected at or near the time of FP] FP, first presentation; OND, other neurological diseases; SD, Standard deviation; RFU, relative fluorescence units; OD, optical density. ^(a)χ² test P = 0.025 versus Brussels. ^(b)χ² test P = 0.01 versus OND ^(c)Verified data regarding occurrence of second relapse within 24 months and EDSS score at 4 years were available only for 41 of the 44 FP patients. ^(d)Mann-Whitney U-test, P = 0.01 versus OND. Cohort-C: Levels of Anti-α-Glucose IgM in FP Patients that Will have a Second Early Relapse (up to 24 Months), Versus Late or No Relapse

Demographics, clinical characteristics, and anti-glycan antibodies levels of FP (n=100) patients who had early relapse (≦24 months, n=58) versus late or no relapse (>24 months, n=42) are described in Table 2.

Except for time to first relapse, no significant differences were found among demographics, clinical characteristics, and square root total IgM of the early versus late relapsing group. Therefore, in this cohort, there was no need to correct for total IgM. Levels of all anti-glycan antibodies were higher in the early versus later relapsers; however, this did not reach statistical significance. To evaluate the possible relationship between levels of anti-α glucose based glycans IgM levels and the risk of imminent (i.e., within 24 months) first relapse, positive patients whose antibodies levels were above cutoff levels were analyzed for at least one of the four antibodies versus patients negative for all four antibodies. Twenty-two (22/58 (38%) early relapsing patients were positive for at least one antibody compared to only 5/42 (12%) positive patients that had a late or no relapse at all, (p=0.003 X² test, odds ratio=4.5 (95% CI [1.5-13.2]), 0.0125 (Bonferroni correction) should be considered the significant threshold for this analysis, since four different methods for determining the cutoff values of the antibodies was applied. In the group of patients who did not experience a second attack within the study period, only one was antibody-positive. Kaplan-Meier survival plot comparison (FIG. 2) between cumulative risk of FP patients who were positive for at least one marker (anti-GAGA4, anti-GAGA2, anti-GAGA3, or anti-GAGA6 IgM) versus negative patients revealed significant differences between the groups (P=0.0025 for up to 24 months respectively) indicating that antibody positive patients consistently had their first relapse earlier. A high level in at least one of the anti-α-glucose IgM antibodies, identified 37.9% of FP patients who had an early attack (<24 months) versus those who had a late or no attack within the follow-up period with 88.1% specificity, 81.5% positive predictive value (PPV), and 50.7% negative predictive value (NPV) (Table 3).

The levels of anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6 IgM antibodies in FP patients are broadly distributed. The subgroup of FP patients with extremely elevated levels of either anti-GAGA2, anti-GAGA3, anti-GAGA4, or anti-GAGA6, relative to the entire FP population, are at a higher risk for faster disease progression (e.g., a higher risk for a relapse in the near future, or an early EDSS score progression) relative to patients that do not have elevated levels of anti-GAGA2, anti-GAGA3, anti-GAGA4, or anti-GAGA6.

Based on the cohort of 100 FP patients, optimized cutoff levels were set for determination of FP patients who have elevated levels of the antibodies compared to the entire FP patient population. Patients were considered as having elevated antibody levels if antibody levels were above a certain cutoff level for at least one of the 4 antibodies. For determination of cutoff levels/values for each antigen, the mean and standard deviation (SD) of the FP population (n=100) were calculated. The cutoff levels were calculated as the mean value of the FP population plus 1, 1.5, or 2 SD, or best fit cutoffs (mean+1.4*SD, mean+1.8*SD, mean+0.8*SD, and mean+1.7*SD for anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6 IgM, respectively). As shown in Table 3, the cutoff level used includes a range (mean+one or two SD). The cutoff level for determination of an extremely elevated antibody level is determined as an upper percentile, e.g., the upper 20%, the upper 15% or the upper 10% of a population of FP patients.

In practice, by testing this cohort of 100 FP patient samples, or any other large enough cohort of FP patients, a reference sera sample similar to the chosen cutoff level is determined for each antibody. Aliquots of the reference sera samples are prepared and kept frozen, for future use. The reference/control sera samples are calibration samples, having a preset antibody level (cutoff level; reference level; control level). When test samples are analyzed for antibody levels, the test sample is examined in parallel with the reference/calibration sample. If the antibody level in the test sample is elevated compared to the antibody level in the reference/calibration sample, the test sample is considered “positive”, e.g., it belongs to the subgroup of FP patients with an elevated level of the antibody.

TABLE 2 Demographic characteristics, clinical characteristics, and anti-glycan antibodies levels of FP (n = 100) patients who had early relapse (≦24 months) versus late or no relapse (>24 months) in cohort-C. Late or no Early relapse, ≦24 m relapse, >24 m (n = 58) (n = 42)^(a) Mean age, years (SD) 34.9 (10.9) 36.2 (8.2) Female, n (%) 43 (74) 29 (69) Center Ottawa, n (%) 22 (38) 23 (54) Square root total IgM, Mean mg/mL (SD) 1.18 (0.26) 1.13 (0.20) Time to relapse, mean months (SD)^(b) 11.4 (7.0) 44.8 (18.8)^(c) Anti-GAGA2 IgM levels, Mean (SD) 2.4 (1.3) 2.2 (0.9) Anti-GAGA3 IgM levels, Mean (SD) 2.5 (1.3) 2.2 (0.9) Anti-GAGA4 IgM levels, Mean (SD) 2.4 (1.3) 2.0 (1.1) Anti-GAGA6 IgM levels, Mean (SD)^(d) 3.1 (1.9) 2.4 (1.1) SD—Standard deviation. ^(a)11 patients remain still FP without drug treatment, 2 patients for 72 months and 9 patients for 48 months. ^(b)Calculated only for patients who had relapse during the follow-up time. ^(c)P < 0.0001, T-test. ^(d)Fisher exact test of Anti-GAGA6 status (positive or negative) vs. early/late relapse, P = 0.015, Odds Ratio 6.4(95% CI 1.4-29.8).

TABLE 3 Diagnostic characteristics for identification of FP patients who had an early relapse (≦24 months, n = 58) vs. late or no relapse (>24 months, n = 42) using a panel of anti-α-glucose disaccharide and different cutoffs (Cohort C). Cutoff used ^(a) Mean + SD Mean + 1.5 SD Mean + 2 SD Best fit Sensitivity, % (95% CI) 37.9 (25.5-51.6) 24.1 (13.9-37.2) 15.5 (7.3-27.4)  37.9 (25.5-51.6) Specificity, % (95% CI) 83.3 (68.6-93.0) 88.1 (74.4-96.0) 95.2 (83.8-99.4) 88.1 (74.4-96.0) PPV, % 75.9 73.7 81.8 81.5 NPV, % 49.3 45.7 44.9 50.7 Efficiency, % 57.0 51.0 49.0 59.0 Relative risk ^(b) 1.5 2.5 3.3 3.2 SD—Standard deviation, ^(a) Cutoffs were calculated for each marker using mean of all 100 FP samples plus 1, 1.5, and 2 SD; PPV, positive predictive value; NPV, negative predictive value. ^(b) Relative risk of patient positive for at least one marker among the 4 markers for having second relapse in up to 24 months, versus negative patients.

In summary, the results of testing a total of 100 frozen sera samples taken from FP patients at or near the time of their first neurological event demonstrate that FP patients with higher levels of a panel of anti-α-glucose IgM antibodies had a higher probability for having a second attack within 24 months, Patients positive for at least one of the anti-α-glucose IgM antibodies had significantly higher cumulative risk (Kaplan-Meier) for having an earlier relapse, identifying about a third of all the early relapsers. An interesting categorical analysis revealed that anti-GAGA6 status alone (positive or negative) had the strongest predictive value of an early relapse. Finding higher levels of serum anti-GAGA2, anti-GAGA3, anti-GAGA4, or anti-GAGA6 IgM antibodies in FP patients predicts, with high specificity, those who will convert to CDMS within 2 years. Such information is invaluable for physicians having some difficulty in deciding upon a course of early therapy for their FP patients.

In the initial discovery phase (Schwarz M et al., 2006 J Neurol Sci, 244:59-68), fourty different glycans were screened. IgM (not IgG or IgA) antibodies to α-glucose antigens could distinguish MS patients from OND controls. This type of IgM response to carbohydrates is most likely produced by self-replenishing B-1 B cells, which respond poorly to protein, but much better to carbohydrate antigens (Goldsby R A et al., 2000 Kuby Immunology, fourth edition W.H. Freeman and Company). In general, B1 B-cells require a high amount of antigen for induction and play an important role as a first line of defense against invading pathogens, removal of senescent cells, cell debris, and other self-antigens (Binder C J et al., 2005 Lipid Res, 46:1353-63; DeFranco A L et al., 2007 Immunity: The Immune Response to Infectious and Inflammatory Disease. New Science Press). Serum derived human IgM monoclonal antibodies were found to accumulate in areas of CNS damage and promote remyelination in demyelinated mice (Pirko I et al., 2004 FASEB J, 18:1577-9; Warrington A E et al., 2007 J Neurosci Res, 85:967-76). This possibly reflects a type of “house-keeping” role, a recognized property of these antibodies (Binder C J et al., 2005 Lipid Res, 46:1353-63). Also, in agreement with the results presented herein, higher levels of IgM antibodies in CSF were found to predict a more severe MS course (Mandrioli J et al., 2008 J Neurol, 255:1023-31). Thus, a test for the presence of anti-α-glucose IgM antibodies in the CSF of MS patients could be useful, though a simple blood test would still be more preferable, especially if it needed to be repeated.

MRI seems sensitive at predicting ultimate vs. imminent conversion to CDMS. Although this cannot be directly compared to the diagnostic performance of antibody measurement in the retrospective study of FP patients (37% sensitivity, 88% specificity, 81% PPV, and 50% NPV), it suggests that measuring anti-α-glucose IgM levels could provide an independent and specific predictive factor for early conversion to CDMS (within 24 months). Finding higher levels of serum anti-GAGA2, anti-GAGA3, anti-GAGA4, or anti-GAGA6 IgM antibodies in FP patients predicts, with high specificity, those who will convert to CDMS within 2 years. Such information might be invaluable for physicians having some difficulty in deciding upon a course of early therapy for their FP patients.

Example 2 Panel of Anti-α-Glucose Based Glycan IgM Antibodies (Anti-GAGA2, Anti-GAGA3, Anti-GAGA4, and Anti-GAGA6) Predict Expanded Disability Status Scale Progression in Clinically Isolated Syndrome Patients Suggestive of Multiple Sclerosis

Prior to the invention, there was no specific serum based biomarker for the prognosis of Expanded Disability Status Scale (EDSS) progression in clinically isolated syndrome (CIS) patients suggestive of Multiple Sclerosis (MS). For patients with CIS suggestive of MS, there is a need to predict the patients with higher risk for rapid progression in EDSS score. As described in detail below, levels of IgM antibodies to a panel of four glucose-based glycans were analyzed for their ability to predict risk of rapid EDSS progression in CIS patients suggestive of MS within 5 years in patients treated early and those that received delayed treatment.

A prospective analysis was performed on 286 sera samples taken from CIS patients at baseline in the Betaferon®/Betaseon® in Newly Emerging MS for Initial Treatment (BENEFIT) study. The patients' EDSS score was determined and they were followed for at least 5 years. Levels of IgM antibodies to glucose based glycan panel (anti-Glc (α1,2) Glc (α) (GAGA2), anti-Glc (α1,3) Glc (α) (GAGA3), anti-Glc (α1,4) Glc (α) (GAGA4), anti-Glc (α1,6) Glc (α) (GAGA6)) in the sera were measured by enzyme immunoassay (EIA). Patients were classified as positive (above cutoff) in one or more antibodies, or negative (below cutoff) for all antibodies.

The results show that 54/286 patients (including those that were treated early and those that received delayed treatment) were classified as positive and 232/286 were classified as negative at baseline. At day 1800, the Kaplan-Meier estimate, i.e., the estimated percentage of patients without confirmed EDSS progression of the group that was classified as negative, was 76.73% compared to only 58.21% in the group classified as positive (P=0.01, log rank test). Cox proportional hazard regression model analysis including all covariates indicate that patients positive for anti-glucose were are at higher risk for progression in EDSS within 5 years (P=0.009, Hazard Ratio (HR) 2.05, CI95% 1.2-3.5). Thus, higher levels of serum anti-α-glucose IgM in CIS patients predicts a high risk of progression in EDSS within 5 years, while lower levels of serum anti-α-glucose IgM in CIS patients predicts a low risk of progression in EDSS within 5 years.

BENEFIT Study and Retained Samples

The BENEFIT study (Betaferon®/Betaseron® in Newly Emerging MS for Initial Treatment) consisted of a placebo-controlled phase and a follow-up phase (Kappos L, et al., 2007 Lancet, 370(9585):389-97). The 5-year double-blinded, placebo-controlled phase, assessed the safety, tolerability, and efficacy of interferon beta-1b 250 μg (8 MIU) delivered subcutaneously every other day in CIS patients with a first event and abnormal magnetic resonance imaging (MRI) scan suggestive of MS. Eligible patients had experienced a first neurological event suggestive of MS and had at least two clinically silent lesions on a T2-weighted brain MRI scan. Within 60 days of the onset of the first clinical event, and after providing written informed consent, patients were randomly assigned, in a 5:3 ratio, to interferon beta-1b 250 μg or placebo subcutaneously every other day. All placebo CIS patients were transferred to the treatment group within 24 months or if they converted to CDMS, whichever came first. After 5 years, CIS patients that began treatment at baseline were defined as “early treatment”, while placebo CIS patients that were transferred to the treatment group due to conversion to CDMS or the passage of 24 months were defined as “delayed treatment”. Thus, the BENEFIT study described herein evaluates breakthrough MS, i.e., MS patients that have a poor prognosis despite treatment. Breakthrough disease does not imply that the patient has not responded to treatment—it implies only that disease activity is present despite therapy.

The study also assessed the long-term course of disease, including delay of disability. During the study, sera from patients with a first clinical event suggestive of multiple sclerosis and at least two clinically silent lesions on T2-weighted brain MRI were retained. Patients were followed up and regular visits were scheduled for the assessment of their EDSS during a 5 year period. Sera samples collected at baseline of the study, along with clinical data from the BENEFIT study were used to evaluate the prediction value of serum anti-α-glucose based glycan IgM antibodies measured at baseline to predict EDSS progression in clinically isolated syndrome patients suggestive of Relapsing Remitting Multiple Sclerosis.

Expanded Disability Status Scale (EDSS)

The Kurtzke Expanded Disability Status Scale (EDSS) (Kurtzke J F, 1983 Neurology, 33(11):1444-52) is a method of quantifying disability in multiple sclerosis. The EDSS quantifies disability in eight Functional Systems (FS) and allows neurologists to assign a Functional System Score (FSS) in each of these. The Functional Systems are: pyramidal, cerebellar, brainstem, sensory, bowel and bladder, visual, cerebral, and other. EDSS steps 1.0 to 4.5 refer to people with MS who are fully ambulatory. EDSS steps 5.0 to 9.5 are defined by an impairment to ambulation. By “progression in EDSS score” is meant a progression from about 1.0 to about 10.0, e.g., about 1.0 to about 5.0; about 5.0 to about 10.0; about 2.0 to about 4.0; about 4.0 to about 6.0; about 6.0 to about 8.0; about 8.0 to about 10.0, about 0.5 to about 1.0; about 1.5 to about 2.5; about 2.0 to about 3.5; or any other incremental increase between 1.0 and 10.0 in increments of 0.5.

Materials and Methods

The study was performed on sera that were collected from patients during the baseline for the BENEFIT study. Study participants were patients who had a clinically isolated syndrome suggestive of MS, having a First Presentation and at least two clinically silent lesions on a T2-weighted brain MRI. Baseline EDSS was between 0 to 5. Patients who met the study inclusion and exclusion criteria were enrolled in the study.

Inclusion Criteria

Subjects enrolled into the BENEFIT study met all of the following inclusion criteria: 1) age between 18 and 45 years inclusive; 2) first clinical episode suggestive of demyelinating disease within the last 60 days (measured from onset of the first episode to treatment start), based on the appearance of a new neurological abnormality which must be present for at least 24 hours. Objective clinical evidence of at least one neurological abnormality other than vegetative or cerebral dysfunction or paresthesia must be present or documented; 3) score of 0 to 5.0 on the Kurtzke Expanded Disability Status Scale’ (EDSS) for at least one time point during the screening period before start of treatment; 4) at least two clinically silent lesions on the T2-weighted brain MRI scan with a size of at least 3 mm, at least one of which is ovoid or periventricular or infratentorial; 5) females of child-bearing potential must agree to practice adequate contraception methods; 6) written informed consent; 7) shipment time of sera was below 3 days.

Exclusion Criteria

Subjects with the following exclusion criteria were excluded from the BENEFIT study:

1) any disease other than MS that could better explain the patient's signs and symptoms; 2) any previous clinical event attributable to acute demyelination in one or more regions of the central nervous system lasting for at least 24h, regardless of whether medical attention was obtained; 3) complete transverse myelitis or bilateral optic neuritis; 4) clinically significant heart diseases; 5) history of severe depression or attempted suicide or current suicidal ideation; 6) clinically significant liver, renal, or bone marrow dysfunction; 7) history of hypergammaglobulinemia; 8) epilepsy not adequately controlled by treatment; 9) any conditions that could interfere with the MRI or any other evaluation in the study; 10) known allergy to gadolinium-DTPA; 11) known allergy to human proteins including albumin and interferons (IFN); 12) intolerance or any contraindication to paracetamol/acetaminophen or ibuprofen; 13) previous participation in this study; 14) participation in any clinical trial within the past six months; 15) pre-treatment with the following substances prior to study enrollment within the following time frames: —at any time—: any IFN, glatiramer acetate (Copolymer I), total lymphoid irradiation, anti-lymphocyte monoclonal antibody treatment (e.g., anti-CD4, anti-CD52 (alemtuzumab), anti-VLA4 (Very Late Antigen-4; natalizumab)), mitoxantrone, cyclophosphamide, azathioprine, Mg, cyclosporine A, methotrexate, or any other immuno modulating or immunosuppressive drug including other recombinant or non-recombinant cytokines; —3 months prior study entry—: any other treatment known to be used for putative or experimental MS treatment including pentoxifylline; 16) pregnancy or lactation; 17) history of alcohol or drug abuse; 18) inability to administer subcutaneous injections either by self or by caregiver; 19) medical, psychiatric or other conditions that compromise the patient's ability to understand the patient information, to comply with the trial protocol, or to complete the study.

Patients were followed for up to 60 months or until CDMS during the placebo-controlled part of the study and were offered to take part in the follow-up phase afterwards. Blood samples were taken from the enrolled patients at baseline visit and at different points in time during the course of the study. Clinical data and EDSS score were taken at screening, baseline and at each follow up visit of the patients. Patient clinical data and MRI includes the following: EDSS, Relapses, T2-weighted brain MRI, T1-weighted brain MRI, Gd enhancing lesions, MS diagnosis according to the McDonald criteria, and CDMS diagnosis. Serum samples attained at baseline (before treatment was initiated) were used for analysis of the gMS panel of glucose IgM antibodies. Sera samples were shipped under ambient temperature to a central lab and were frozen.

Enzyme Immuno-Assay for Anti-GAGA 2, Anti-GAGA 3, Anti-GAGA 4, and Anti-GAGA 6 IgM Levels

Each sample was tested for levels of anti-GAGA: anti-GAGA2, anti-GAGA 3, anti-GAGA 4, and anti-GAGA 6 μM using the enzyme immuno-assay (EIA) method in a 96 micro-well plate platform. 96 micro-well plates were prepared as follows: 96-well microwell plates with the antigens of GAGA2, GAGA3, GAGA4, and GAGA6 were covalently bound by a linker to the wells walls as previously described (Schwarz, et al., 2003 Glycobiology, 13(11): 749-754). Sera measurements were done in duplicate, each plate included 5 points calibration curve. Results were reported in arbitrary EIA Units (EU).

EIA Procedures

The serum samples were stored frozen and were transported frozen to Glycominds Ltd., Lod, Israel. Samples were stored in −80° C. until use. Prior to use, the samples were thawed by incubation in 37° C. for 2 hours. IgG depletion was performed using a commercial mini Rapi-Sep® units (PanBio, Baltimore, Md., USA). Serum samples were diluted 1:1200 in a sample diluent, dispensed into the wells in duplicates, and incubated for 180 min in 4° C., then washed with wash buffer. Bound antibodies were labeled with horseradish peroxidase (HRP)-conjugated goat anti-human IgM type-specific antibody, washed, and 3,3′,5,5′-tetramethylbenzidine was added for detection. After 30 minutes, the enzymatic reaction was stopped with 1% sulfuric acid solution and optical density (OD) in the wells was read at 450 nm with a microwell plate reader (Wallac, Turku, Finland). Each plate included a 5 point calibration curve with a preset unit value for each point on the curve. The calibration curve used to calculate the EIA units (EU), and to normalize results obtained from different plates. Results were reported in EU.

Statistical Analysis

Numerical variables were compared across groups by Student's t-test or by the Mann-Whitney U-test, depending on whether or not they followed a normal distribution, and the X² test for rates comparison between groups. P-values of less than 0.05 were considered to be statistically significant. Uncertainty of results was expressed by 95% confidence intervals. Diagnostic accuracy was calculated by sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Based on the anti-GAGA2, anti-GAGA3, anti-GAGA4, and anti-GAGA6, EU values measured for each sample, the following classification rule was used (Classifier 1): Samples were considered as positive if results were above cutoff levels for at least one of the 4 antibodies. Cutoff values for each antigen were calculated based on the mean and standard deviation values of the full population. Thus, the following rules, which were determined based on results in Table 4 (best fit column) were used for determining cutoff values: GAGA2: Mean+1.4*SD; GAGA3: Mean+1.8*SD; GAGA4: Mean+1.7*SD; and GAGA6: Mean+0.8*SD. These cutoff values were determined according to the total population, both treatment and placebo group. The cumulative risk of confirmed progression to 1.0 EDSS was calculated for the positive group according to classifier 1, and to the negative group according to classifier 1, according to the Kaplan-Meier method, and differences between the groups were evaluated in a univariate analysis by the log-rank test.

Cox proportional hazards regression models were applied to time to confirmed EDSS progression in order to assess whether classifier 1 groups (≦cut-off in at least one of the 4 antibodies, versus>cut-off) predict the progression of the disease.

It was determined if the distribution of the “time-to-event” times in the classifier 1 groups differ, and how the effects of the different classifier 1 groups act on the hazard. The Cox proportional hazards regression models will include adjustments for the following baseline covariates: age, sex, onset of disease (multi- vs. monofocal), use or nonuse of corticosteroid treatment at the first event, the effect of early versus delayed treatment with IFNB-1b, the presence of gadolinium-enhancing lesions on T1-weighted MRI scans at baseline (no versus at least one lesion), and the number of hyperintense lesions on T2-weighted scans (less than nine versus at least nine T2 lesions).

Results

A total of 286 base line samples from patients after first neurological event suggestive of MS were included in the study. The overall distribution of anti-GAGA 2, anti-GAGA 3, anti-GAGA 4, and anti-GAGA 6 IgM levels in the study population is described in Table 5. The frequency of base line and MRI screening parameters in the total population and by classifier 1 (positive/negative) is described in Table 6. The results of the Kaplan-Meier estimate analysis for day 1800 is described in FIG. 3 and Table 7.

54/286 patients were classified as positive and 232/286 were classified as negative at base line (Table 8). Table 9 describes the detailed data. At day 1800, the Kaplan-Meier estimate, i.e., the estimated percentage of patients without confirmed EDSS progression of the group that was classified as negative was 76.73%, compared to only 58.21% in the group classified as positive (P=0.01, log rank test). Cox proportional hazard regression model analysis, including all covariates, indicates that classifier 1 contributed very significantly (P=0.009, Hazard Ratio (HR) 2.05, C195% 1.2-3.5) to model for prediction of time to confirmed EDSS progression in the evaluated population. In addition, treatment, age and the presence of Gd-enhancing lesions at screening contributed significantly to the model fit. See, Tables 10 and 11.

Both Kaplan-Meier survival and Cox proportional hazard analysis indicate that patients with higher levels of anti-GAGA 2, anti-GAGA 3, anti-GAGA 4, or anti-GAGA 6 have a higher risk for progression in EDSS within 5 years. These results indicate that classifying patients based on anti-α-glucose IgM levels provides an independent and more specific risk predictive factor for early one point progression in EDSS (within 5 years). Finding higher levels of serum anti-GAGA 2, anti-GAGA3, anti-GAGA4, or anti-GAGA6 IgM antibodies in CIS patients predicts those who will progress in their EDSS within 5 years. This information is invaluable for disease management and the prediction of risk for rapid EDSS MS progression and for breakthrough MS.

In one aspect, referring to FIG. 4, a process (100) for determining if a CIS patient is at risk for a conversion to CDMS within 24 months includes the stages shown. In the process (100), a level of at least one antibody in a patient sample, e.g., blood, is compared to a reference level. The process (100), however, is exemplary only and not limiting. The process (100) may be altered, e.g., by having stages added, removed, or rearranged. In an embodiment, the process (100) is implemented on a system including one or more computers or computer networks configured to read and execute computer-readable instructions stored on computer-readable mediums. A computer-readable medium is defined as any kind of computer memory such as floppy disks, conventional hard disks, CD and DVD ROMS, Flash ROMS, nonvolatile ROM, and RAM. The computers can be configured to receive data from a user via a user interface, or automatically via machine interface (e.g., a USB connection to a blood analyzer).

At stage 102, a test sample (e.g., whole blood, serum, or plasma) is obtained from a patient. Optionally, the patient's blood sample is collected by venipuncture. Following blood collection, the blood will clot, and the serum is separated from the blood clot by centrifugation of the collection tube. Sera samples are collected from a patient and stored prior to use, e.g., the sample is stored at −20° C., −70° C. or −80° C. until use. At stage 104, the level of at least one antibody is measured. For example, in an embodiment, levels of an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody are detected. Optionally, levels of an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, or an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody are detected.

At stage 106, the level of at least one antibody in the test sample is compared to one or more reference levels. Optionally, a computer or computer network is used to make the comparison. For example, the reference levels are stored on a computer-readable medium associated with computer or computer network, and the patient's at least one antibody level is received via an input device (e.g., keyboard, GUI, COM port).

At stage 108, the patient's risk factor for progressing to CDMS is determined. For example, if the level of at least one of the antibodies in the test sample is higher than a reference level, then the patient is at higher risk of progressing to CDMS within 24 months. In an embodiment, a computer can be used to generate an output (e.g., report, message, display) to indicate the corresponding risk factor.

In another aspect, referring to FIG. 5, a process (110) for determining if a patient is likely to progress in EDSS score within twenty years includes the stages shown. In the process (110), a level of at least one antibody in the patient sample, e.g., blood, is compared to a reference level. The process (110), however, is exemplary only and not limiting. The process (110) may be altered, e.g., by having stages added, removed, or rearranged. In an embodiment, the process (110) is implemented on a system including one or more computers or computer networks configured to read and execute computer-readable instructions stored on computer-readable mediums. A computer-readable medium is defined as any kind of computer memory such as floppy disks, conventional hard disks, CD and DVD ROMS, Flash ROMS, nonvolatile ROM, and RAM. The computers can be configured to receive data from a user via a user interface, or automatically via machine interface (e.g., a USB connection to a blood analyzer).

At stage 112, a test sample (e.g., whole blood, serum, or plasma) is obtained from a patient. At stage 114, the level of at least one antibody is measured. For example, in an embodiment, levels of an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody are detected. Optionally, levels of an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, or an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody are detected.

At stage 116, the level of at least one antibody in the test sample is compared to one or more reference levels. Optionally, a computer or computer network is used to make the comparison. For example, the reference levels are stored on a computer-readable medium associated with computer or computer network, and the patient's at least one antibody level is received via an input device (e.g., keyboard, GUI, COM port).

At stage 118, the patient's risk factor for progressing in EDSS score is determined. For example, if the level of at least one of the antibodies in the test sample is higher than a reference level, then the patient is at higher risk of progressing in EDSS score within twenty years. In an embodiment, a computer can be used to generate an output (e.g., report, message, display) to indicate the corresponding risk factor.

In another aspect, referring to FIG. 6, a process (120) for determining if a patient is likely to develop breakthrough MS includes the stages shown. In the process (120), a level of at least one antibody in the patient sample, e.g., blood, is compared to a reference level. The process (120), however, is exemplary only and not limiting. The process (120) may be altered, e.g., by having stages added, removed, or rearranged. In an embodiment, the process (120) is implemented on a system including one or more computers or computer networks configured to read and execute computer-readable instructions stored on computer-readable mediums. A computer-readable medium is defined as any kind of computer memory such as floppy disks, conventional hard disks, CD and DVD ROMS, Flash ROMS, nonvolatile ROM, and RAM. The computers can be configured to receive data from a user via a user interface, or automatically via machine interface (e.g., a USB connection to a blood analyzer).

At stage 122, a test sample (e.g., whole blood, serum, or plasma) is obtained from a patient. At stage 124, the level of at least one antibody is measured. For example, in an embodiment, levels of an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody are detected. Optionally, levels of an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, or an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody are detected.

At stage 126, the level of at least one antibody in the test sample is compared to one or more reference levels. Optionally, a computer or computer network is used to make the comparison. For example, the reference levels are stored on a computer-readable medium associated with computer or computer network, and the patient's at least one antibody level is received via an input device (e.g., keyboard, GUI, COM port).

At stage 128, the patient's risk factor for developing breakthrough MS is determined. For example, if the level of at least one of the antibodies in the test sample is higher than a reference level, then the patient is at higher risk of developing breakthrough MS. In an embodiment, a computer can be used to generate an output (e.g., report, message, display) to indicate the corresponding risk factor.

TABLE 4 Anti-GAGA2, anti-GAGA3, anti-GAGA4 and anti-GAGA6 levels in the placebo and IFNB-1b treatment. arms of BENEFIT study. Treatment n Nmiss Mean SD Min Median Max Anti-GAGA 2 [EIA(EU)] Placebo 109 0 68.57140 58.331532 −11.6015 57.93817 274.4357 IFNB-1b 177 0 65.04004 59.359072 −12.0682 46.65570 352.5607 Total 286 0 66.38591 58.891945 −12.0682 50.21277 352.5607 Anti-GAGA 3 [EIA(EU)] Placebo 109 0 71.11539 55.016680 −9.1998 58.15728 336.0155 IFNB-1b 177 0 66.86592 52.483847 −8.2925 51.51775 315.0962 Total 286 0 68.48548 53.407335 −9.1998 56.58532 336.0155 Anti-GAGA 4 [EIA(EU)] Placebo 109 0 78.92899 67.735684 −0.8989 65.95567 402.3137 IFNB-1b 177 0 72.49212 76.716617 −11.5659 55.49377 667.4310 Total 286 0 74.94533 73.368802 −11.5659 58.57662 667.4310 Anti-GAGA 6 [EIA(EU)] Placebo 109 0 66.74723 58.274748 −14.6643 51.26553 327.1028 IFNB-1b 177 0 65.41337 61.363594 −13.2663 49.00671 407.4335 Total 286 0 65.92173 60.105421 −14.6643 51.10276 407.4335

TABLE 5 Frequency table of base line and MRI screening parameters in the total population (n = 286) and by classifier 1 (positive/negative). Negative Positive Total Sex Number of subjects 232 (100.0%) 54 (100.0%) 286 (100.0%) male 69 (29.7%) 8 (14.8%) 77 (26.9%) female 163 (70.3%) 46 (85.2%) 209 (73.1%) Treatment Number of subjects 232 (100.0%) 54 (100.0%) 286 (100.0%) Placebo 87 (37.5%) 22 (40.7%) 109 (38.1%) IFNB-1b 145 (62.5%) 32 (59.3%) 177 (61.9%) Steroid use during first event Number of subjects 232 (100.0%) 54 (100.0%) 286 (100.0%) no 66 (28.4%) 9 (16.7%) 75 (26.2%) yes 166 (71.6%) 45 (83.3%) 211 (73.8%) onset of disease in 304747 Number of subjects 232 (100.0%) 54 (100.0%) 286 (100.0%) monofocal 128 (55.2%) 26 (48.1%) 154 (53.8%) multifocal 104 (44.8%) 28 (51.9%) 132 (46.2%) CSF result Number of subjects 166 (100.0%) 33 (100.0%) 199 (100.0%) negative 29 (17.5%) 2 (6.1%) 31 (15.6%) positive 137 (82.5%) 31 (93.9%) 168 (84.4%) >=1 Gd-enhancing lesion at screening Number of subjects 230 (100.0%) 54 (100.0%) 284 (100.0%) no 130 (56.5%) 34 (63.0%) 164 (57.7%) yes 100 (43.5%) 20 (37.0%) 120 (42.3%)

TABLE 6 Kaplan-Meier product limit estimates at day 1800 and log rank P- value for comparison between patients positive for classifier 1 versus negative with respect to time to confirmed EDSS progression. Time-to-event outcome #events and Kaplan-Meier estimates at day 1800 Test result Negative Positive (p-value) of (n = 232) (n = 54) log-rank test Time to confirmed EDSS 49/76.73% 20/58.21% 0.011664 progression

TABLE 7 Kaplan-Meier analysis of time to confirmed EDSS progression - data summary. Survival Number Number confirmed Standard Day Classifier 1 at Risk EDSS progression Survival Error 0 <=cut-off 1 232 0 1.0000 0    >cut-off 1 54 0 1.0000 0    90 <=cut-off 1 225 3 0.9869  0.00750 >cut-off 1 51 1 0.9815 0.0183 180 <=cut-off 1 218 7 0.9693 0.0114 >cut-off 1 47 3 0.9422 0.0324 270 <=cut-off 1 208 15 0.9337 0.0165 >cut-off 1 47 3 0.9422 0.0324 360 <=cut-off 1 204 17 0.9247 0.0176 >cut-off 1 46 4 0.9221 0.0374 450 <=cut-off 1 200 20 0.9111 0.0190 >cut-off 1 43 7 0.8620 0.0485 540 <=cut-off 1 197 22 0.9019 0.0199 >cut-off 1 43 7 0.8620 0.0485 630 <=cut-off 1 191 27 0.8790 0.0219 >cut-off 1 40 8 0.8420 0.0513 720 <=cut-off 1 177 31 0.8599 0.0234 >cut-off 1 39 8 0.8420 0.0513 810 <=cut-off 1 169 34 0.8452 0.0245 >cut-off 1 35 12 0.7556 0.0616 900 <=cut-off 1 167 35 0.8401 0.0248 >cut-off 1 35 12 0.7556 0.0616 990 <=cut-off 1 163 37 0.8301 0.0255 >cut-off 1 33 14 0.7124 0.0652 1080 <=cut-off 1 159 38 0.8249 0.0259 >cut-off 1 32 15 0.6908 0.0667 1170 <=cut-off 1 156 41 0.8093 0.0269 >cut-off 1 31 16 0.6692 0.0680 1260 <=cut-off 1 156 41 0.8093 0.0269 >cut-off 1 31 16 0.6692 0.0680 1350 <=cut-off 1 154 43 0.7990 0.0276 >cut-off 1 30 17 0.6477 0.0692 1440 <=cut-off 1 151 45 0.7886 0.0282 >cut-off 1 28 19 0.6045 0.0710 1530 <=cut-off 1 149 45 0.7886 0.0282 >cut-off 1 26 20 0.5821 0.0718 1620 <=cut-off 1 149 45 0.7886 0.0282 >cut-off 1 26 20 0.5821 0.0718 1710 <=cut-off 1 144 48 0.7673 0.0291 >cut-off 1 25 20 0.5821 0.0718 1800 <=cut-off 1 96 49 0.7726 0.0293 >cut-off 1 19 20 0.5821 0.0718 1890 <=cut-off 1 2 49 0.7673 0.0293 >cut-off 1 1 20 0.5821 0.0718 1980 <=cut-off 1 0 49 . . >cut-off 1 0 20 . .

TABLE 8 BENEFIT study raw data Anti- Anti- Anti- Anti- Classifier GAGA2 GAGA2 > 148.8, GAGA3 GAGA3 > 164.6, GAGA4 GAGA4 > 133.6, GAGA6 GAGA6 > 168.1, status, Patient IgM Y = Yes, IgM Y = Yes, IgM Y = Yes, IgM Y = Yes, P = positive, # ID Units N = No Units N = No Units N = No Units N = No N = negative 1 15060 36.0 N 36.1 N 16.5 N 37.8 N N 2 15062 44.2 N 51.3 N 33.8 N 85.9 N N 3 15063 57.9 N 91.3 N 73.0 N 143.8 N N 4 15064 75.9 N 148.7 N 65.5 N 212.4 Y P 5 15065 15.3 N 47.2 N 8.1 N 21.8 N N 6 15066 23.5 N 6.2 N 8.6 N 9.2 N N 7 15067 14.5 N 74.0 N 0.5 N 99.7 N N 8 15068 13.1 N 15.1 N 5.4 N 14.8 N N 9 15070 92.1 N 15.8 N 82.9 N 37.0 N N 10 15071 83.0 N 35.7 N 49.0 N 75.3 N N 11 15072 25.3 N 79.8 N 35.2 N 88.8 N N 12 15073 13.3 N 161.8 N 19.5 N 136.7 N N 13 15075 13.4 N 18.4 N 10.8 N 15.8 N N 14 15076 10.2 N 27.0 N 3.2 N 79.3 N N 15 15077 10.6 N 19.7 N 14.4 N 24.5 N N 16 15078 47.5 N 13.2 N 73.8 N 21.1 N N 17 15079 22.9 N 28.1 N 28.8 N 49.0 N N 18 15080 37.4 N 156.0 N 61.1 N 234.0 Y P 19 15081 31.4 N 58.4 N 53.2 N 31.2 N N 20 15082 8.5 N 41.5 N 23.8 N 19.2 N N 21 15083 45.7 N 8.6 N 83.3 N 15.7 N N 22 15084 34.8 N 22.7 N 59.3 N 45.7 N N 23 15085 8.5 N 80.9 N 27.4 N 204.5 Y P 24 15086 29.2 N 4.2 N 28.7 N 14.9 N N 25 15087 33.4 N 58.2 N 37.8 N 104.6 N N 26 15088 24.8 N 100.0 N 28.7 N 85.0 N N 27 15089 5.0 N 98.0 N 4.1 N 44.9 N N 28 15091 21.5 N 42.9 N 36.7 N 51.3 N N 29 15092 −12.1 N 80.3 N −11.6 N 102.3 N N 30 15094 6.1 N 56.8 N 26.3 N 72.9 N N 31 15095 91.1 N 22.9 N 134.6 Y 29.4 N P 32 15097 21.2 N 59.1 N 49.2 N 178.3 Y P 33 15098 49.2 N 71.4 N 50.4 N 34.8 N N 34 15099 15.3 N 55.3 N 47.2 N 14.8 N N 35 15100 11.6 N 41.0 N 28.8 N 10.3 N N 36 15101 6.5 N 31.0 N 12.9 N 19.3 N N 37 15102 21.5 N 39.9 N 34.4 N 21.1 N N 38 15104 67.7 N 35.8 N 141.5 Y 62.7 N P 39 15105 38.6 N 72.2 N 51.8 N 45.7 N N 40 15106 6.1 N 25.0 N 33.8 N 2.6 N N 41 15107 2.3 N 38.8 N 23.1 N 21.7 N N 42 15108 32.5 N 9.1 N 58.6 N 1.8 N N 43 15110 105.4 N 22.7 N 162.2 Y 50.9 N P 44 15111 59.9 N 125.3 N 186.0 Y 112.6 N P 45 15113 28.7 N 45.0 N 36.9 N 31.3 N N 46 15114 7.0 N 16.0 N 13.6 N 37.5 N N 47 15115 15.6 N 9.5 N 58.5 N 29.8 N N 48 15116 26.3 N 31.2 N 21.8 N 68.4 N N 49 15117 43.9 N 40.8 N 43.1 N 407.4 Y P 50 15118 211.4 Y 59.0 N 667.4 Y 89.7 N P 51 15119 30.6 N 15.5 N 269.1 Y 40.3 N P 52 15120 0.4 N 86.0 N 77.2 N 84.2 N N 53 15121 21.4 N 59.6 N 55.5 N 12.0 N N 54 15123 29.5 N −8.3 N 112.3 N −13.3 N N 55 15124 11.3 N 17.1 N 43.5 N 47.6 N N 56 15125 24.0 N 36.5 N 49.7 N 16.6 N N 57 15126 32.8 N 22.0 N 49.1 N 3.9 N N 58 15127 52.4 N 66.3 N 115.1 N 37.2 N N 59 15128 72.0 N 127.9 N 88.2 N 56.0 N N 60 15129 8.0 N 30.5 N 5.7 N 35.1 N N 61 15130 147.3 N 38.7 N 209.3 Y 20.8 N P 62 15131 15.5 N 78.1 N 53.3 N 63.5 N N 63 15132 1.7 N 78.0 N 6.7 N 64.9 N N 64 15133 19.7 N 23.4 N 24.7 N 17.5 N N 65 15134 18.6 N 95.8 N 22.9 N 85.2 N N 66 15135 131.1 N 46.3 N 247.7 Y 44.2 N P 67 15136 36.8 N 52.8 N 82.4 N 37.0 N N 68 15137 12.3 N 59.9 N 15.4 N 81.1 N N 69 15138 3.9 N 36.8 N 34.9 N 32.8 N N 70 15139 52.0 N 14.5 N 69.2 N 20.9 N N 71 15141 80.6 N 96.2 N 187.9 Y 122.8 N P 72 15142 82.2 N 37.0 N 152.0 Y 46.1 N P 73 15143 61.7 N 70.5 N 136.0 Y 52.0 N P 74 15144 50.3 N 71.7 N 72.5 N 61.0 N N 75 15145 24.1 N 61.0 N 15.9 N 37.7 N N 76 15146 13.6 N 21.6 N 23.7 N 12.2 N N 77 15147 19.1 N 54.2 N 19.3 N 10.4 N N 78 15148 75.4 N 48.2 N 35.4 N 100.0 N N 79 15149 134.7 N 47.7 N 62.6 N 82.7 N N 80 15150 106.8 N 38.0 N 65.8 N 108.2 N N 81 15151 25.6 N 22.7 N 36.3 N 48.1 N N 82 15152 135.7 N 137.8 N 121.5 N 102.8 N N 83 15153 121.9 N 118.7 N 63.2 N 53.8 N N 84 15154 144.1 N 114.6 N 74.0 N 53.0 N N 85 15156 126.5 N 39.4 N 42.1 N 121.5 N N 86 15157 191.1 Y 141.7 N 248.5 Y 123.9 N P 87 15158 66.2 N 3.8 N 29.9 N 63.5 N N 88 15159 33.1 N 18.5 N 35.3 N 26.3 N N 89 15160 20.4 N 29.0 N 39.6 N 28.5 N N 90 15161 14.9 N 16.1 N 11.1 N 6.4 N N 91 15162 52.5 N 68.5 N 32.0 N 56.7 N N 92 15163 187.8 Y 278.2 Y 224.4 Y 99.9 N P 93 15164 4.6 N 6.7 N 3.9 N −0.3 N N 94 15166 274.4 Y 130.0 N 114.0 N 106.0 N P 95 15167 18.3 N 42.8 N 21.5 N 16.8 N N 96 15169 118.1 N 102.4 N 82.4 N 86.1 N N 97 15170 127.8 N 183.7 Y 57.2 N 80.8 N P 98 15171 46.1 N 93.6 N 44.3 N 71.2 N N 99 15172 4.0 N 15.0 N 18.2 N 7.3 N N 100 15173 66.3 N 58.1 N 58.3 N 69.9 N N 101 15174 37.3 N 30.3 N 51.1 N 38.7 N N 102 15175 2.0 N 5.9 N 19.9 N 4.1 N N 103 15176 40.0 N 41.0 N 80.1 N 43.4 N N 104 15177 352.6 Y 278.7 Y 417.1 Y 348.3 Y P 105 15178 150.9 Y 125.9 N 215.6 Y 83.8 N P 106 15179 143.0 N 113.6 N 98.8 N 124.5 N N 107 15180 59.2 N 33.4 N 34.9 N 35.5 N N 108 15181 95.4 N 44.9 N 70.7 N 110.1 N N 109 15182 72.2 N 59.4 N 59.7 N 78.5 N N 110 15183 65.2 N 59.9 N 120.3 N 34.5 N N 111 15184 87.2 N 120.3 N 122.6 N 88.6 N N 112 15185 67.7 N 14.8 N 34.7 N 45.0 N N 113 15186 63.0 N 37.1 N 53.7 N 55.5 N N 114 15187 12.7 N 7.7 N 21.6 N 1.9 N N 115 15188 58.6 N 33.8 N 62.7 N 53.2 N N 116 15189 24.8 N 19.9 N 20.6 N 18.1 N N 117 15191 44.2 N 36.3 N 59.8 N 33.8 N N 118 15192 127.7 N 107.8 N 228.3 Y 178.6 Y P 119 15193 52.0 N 43.3 N 73.1 N 66.9 N N 120 15194 47.6 N 87.7 N 54.4 N 46.7 N N 121 15195 63.2 N 15.3 N 41.7 N 78.6 N N 122 15196 114.3 N 82.2 N 165.9 Y 121.3 N P 123 15197 96.7 N 51.5 N 38.0 N 119.4 N N 124 15198 22.6 N 6.9 N 7.9 N 11.5 N N 125 15199 193.1 Y 69.8 N 104.4 N 131.1 N P 126 15200 8.6 N 13.5 N 21.2 N 6.9 N N 127 15201 68.0 N 66.2 N 104.2 N 55.0 N N 128 15202 50.0 N 90.7 N 99.7 N 18.6 N N 129 15203 249.5 Y 88.0 N 191.6 Y 157.3 N P 130 15204 47.5 N 45.2 N 33.3 N 20.7 N N 131 15205 199.0 Y 47.4 N 191.3 Y 62.6 N P 132 15207 140.9 N 35.8 N 55.0 N 44.1 N N 133 15208 32.6 N 8.0 N 62.2 N 34.9 N N 134 15209 167.3 Y 30.6 N 160.0 Y 109.9 N P 135 15211 29.7 N 31.2 N 43.4 N 11.8 N N 136 15212 33.2 N 60.0 N 85.7 N 29.5 N N 137 15213 46.4 N 8.0 N 59.3 N 30.6 N N 138 15214 30.3 N 17.3 N 22.3 N 10.1 N N 139 15215 31.8 N 6.4 N 24.0 N 35.5 N N 140 15216 50.1 N 33.2 N 69.6 N 35.2 N N 141 15217 60.4 N 52.3 N 38.2 N 67.8 N N 142 15218 95.3 N 122.0 N 114.4 N 106.5 N N 143 15219 97.6 N 154.4 N 93.8 N 100.6 N N 144 15220 52.6 N 46.9 N 38.1 N 73.0 N N 145 15221 7.9 N 6.7 N 7.6 N 0.4 N N 146 15222 92.8 N 65.9 N 101.5 N 100.1 N N 147 15223 38.5 N 56.3 N 64.0 N 44.3 N N 148 15224 17.7 N 29.2 N 16.2 N 9.7 N N 149 15225 37.9 N 74.3 N 116.9 N 62.2 N N 150 15226 10.5 N 28.1 N 107.2 N 75.7 N N 151 15227 20.6 N 72.7 N 72.2 N 52.4 N N 152 15229 13.1 N 38.6 N 49.8 N 29.8 N N 153 15230 6.8 N 24.4 N 34.4 N 8.9 N N 154 15231 39.4 N 124.0 N 66.6 N 35.8 N N 155 15232 86.1 N 64.7 N 56.6 N 41.7 N N 156 15233 16.6 N 15.2 N 16.9 N 0.1 N N 157 15234 107.1 N 60.4 N 79.6 N 44.8 N N 158 15236 82.3 N 96.6 N 86.3 N 46.0 N N 159 15237 66.4 N 86.2 N 51.1 N 27.5 N N 160 15238 81.7 N 74.1 N 62.9 N 111.4 N N 161 15239 59.8 N 56.2 N 82.6 N 49.8 N N 162 15240 24.8 N 9.3 N 8.6 N −1.0 N N 163 15241 89.6 N 47.1 N 94.4 N 30.2 N N 164 15242 77.6 N 219.4 Y 75.4 N 56.6 N P 165 15243 111.6 N 67.9 N 78.8 N 60.4 N N 166 15244 203.6 Y 134.1 N 125.8 N 164.1 N P 167 15245 152.1 Y 151.4 N 120.3 N 120.2 N P 168 15246 165.3 Y 125.3 N 377.0 Y 122.2 N P 169 15247 51.5 N 42.5 N 51.1 N 27.3 N N 170 15248 96.9 N 110.6 N 218.1 Y 119.0 N P 171 15249 54.5 N 119.5 N 116.7 N 118.9 N N 172 15250 97.5 N 87.5 N 110.5 N 72.1 N N 173 15252 221.2 Y 336.0 Y 243.1 Y 172.2 Y P 174 15253 22.4 N 34.8 N 66.2 N 51.7 N N 175 15254 57.3 N 68.3 N 129.5 N 107.8 N N 176 15255 −2.5 N 12.8 N 7.1 N −5.1 N N 177 15256 69.7 N 36.5 N 48.1 N 52.0 N N 178 15257 77.7 N 57.7 N 69.0 N 79.4 N N 179 15258 44.5 N 39.0 N 54.7 N 70.1 N N 180 15259 22.0 N 36.4 N 12.6 N 14.9 N N 181 15260 262.7 Y 224.3 Y 179.0 Y 341.6 Y P 182 15262 100.9 N 121.6 N 85.6 N 151.2 N N 183 15263 186.2 Y 154.7 N 149.9 Y 228.2 Y P 184 15264 30.8 N 21.5 N 40.4 N 22.6 N N 185 15265 99.2 N 30.7 N 155.4 Y 128.7 N P 186 15266 79.6 N 139.0 N 86.0 N 95.1 N N 187 15267 172.4 Y 183.0 Y 161.1 Y 168.1 N P 188 15268 18.6 N 35.8 N 18.6 N 31.3 N N 189 15269 60.7 N 36.7 N 30.8 N 37.7 N N 190 15271 106.4 N 76.4 N 102.6 N 90.9 N N 191 15272 138.0 N 77.3 N 77.5 N 48.2 N N 192 15273 33.7 N 32.5 N 26.9 N 9.9 N N 193 15275 33.0 N 49.6 N 14.1 N 1.8 N N 194 15277 161.9 Y 129.3 N 166.2 Y 93.9 N P 195 15278 81.6 N 59.0 N 66.0 N 21.7 N N 196 15279 76.7 N 51.1 N 92.7 N 46.7 N N 197 15280 83.3 N 84.2 N 86.3 N 66.6 N N 198 15281 37.0 N 103.4 N 64.2 N 87.1 N N 199 15282 −1.2 N 14.6 N 2.6 N −6.6 N N 200 15283 44.3 N 105.8 N 58.8 N 141.1 N N 201 15284 30.6 N 154.9 N 44.0 N 35.7 N N 202 15285 22.1 N 35.9 N 13.8 N 32.1 N N 203 15286 13.6 N 78.2 N 36.5 N 27.1 N N 204 15287 215.5 Y 90.5 N 93.7 N 71.6 N P 205 15288 28.1 N 62.5 N 42.0 N 60.8 N N 206 15290 40.5 N 28.1 N 18.4 N 4.5 N N 207 15291 46.3 N 235.7 Y 75.9 N 81.7 N P 208 15292 19.7 N 42.7 N 17.4 N 4.0 N N 209 15293 87.1 N 116.8 N 86.2 N 46.7 N N 210 15294 48.1 N 68.0 N 52.0 N 30.8 N N 211 15295 169.3 Y 172.4 Y 166.1 Y 157.6 N P 212 15296 26.6 N 84.5 N 40.5 N 26.9 N N 213 15297 −11.6 N −9.2 N −0.9 N −14.7 N N 214 15298 4.5 N 26.4 N 16.8 N −4.3 N N 215 15299 88.0 N 165.7 Y 123.5 N 72.5 N P 216 15300 104.7 N 101.0 N 73.0 N 48.8 N N 217 15301 59.0 N 44.1 N 29.3 N 7.5 N N 218 15302 291.1 Y 91.5 N 137.2 Y 188.9 Y P 219 15303 46.8 N 48.9 N 58.0 N 26.8 N N 220 15304 92.1 N 124.4 N 199.2 Y 101.0 N P 221 15305 103.9 N 147.3 N 140.7 Y 156.4 N P 222 15306 220.1 Y 126.9 N 212.4 Y 173.9 Y P 223 15307 94.1 N 97.9 N 101.5 N 56.8 N N 224 15308 148.1 N 78.9 N 148.4 Y 71.2 N P 225 15309 20.0 N 10.1 N 20.9 N 5.6 N N 226 15310 52.4 N 18.3 N 53.7 N 21.4 N N 227 15311 92.2 N 57.8 N 106.7 N 79.5 N N 228 15312 83.0 N 140.6 N 84.1 N 116.1 N N 229 15500 89.1 N 85.5 N 84.5 N 69.3 N N 230 15501 86.1 N 121.8 N 89.7 N 132.8 N N 231 15502 49.7 N 43.4 N 60.4 N 38.9 N N 232 15503 28.1 N 47.9 N 22.5 N 51.3 N N 233 15504 30.3 N 34.7 N 14.8 N 17.1 N N 234 15505 18.4 N 22.5 N 19.7 N 28.5 N N 235 15506 19.1 N 26.0 N 17.6 N 20.9 N N 236 15507 70.7 N 55.6 N 79.8 N 85.4 N N 237 15508 86.1 N 115.1 N 131.0 N 112.6 N N 238 15509 146.2 N 128.0 N 73.9 N 90.8 N N 239 15511 46.0 N 24.4 N 20.3 N 16.8 N N 240 15512 248.7 Y 315.1 Y 304.8 Y 258.9 Y P 241 15513 15.9 N 28.3 N 10.7 N 12.4 N N 242 15514 57.3 N 97.2 N 110.3 N 63.0 N N 243 15515 152.5 Y 195.6 Y 214.7 Y 231.7 Y P 244 15516 45.1 N 80.9 N 64.1 N 47.4 N N 245 15517 60.9 N 35.7 N 75.7 N 45.3 N N 246 15518 106.9 N 88.0 N 103.4 N 79.3 N N 247 15519 40.9 N 22.7 N 83.4 N 75.1 N N 248 15520 93.2 N 76.0 N 20.9 N 19.1 N N 249 15521 67.7 N 62.4 N 119.7 N 61.2 N N 250 15522 14.4 N 42.7 N 37.5 N 30.1 N N 251 15523 70.5 N 50.5 N 61.5 N 59.2 N N 252 15524 19.8 N 85.7 N 25.6 N 36.9 N N 253 15525 71.1 N 89.6 N 52.4 N 73.7 N N 254 15526 20.1 N 29.3 N 21.4 N 28.3 N N 255 15527 63.5 N 93.6 N 61.1 N 51.4 N N 256 15528 97.2 N 93.6 N 46.0 N 119.4 N N 257 15529 16.5 N 63.4 N 10.6 N 22.3 N N 258 15530 99.6 N 139.4 N 75.8 N 158.9 N N 259 15531 43.7 N 47.3 N 22.1 N 30.0 N N 260 15532 46.7 N 44.1 N 45.2 N 52.4 N N 261 15533 213.8 Y 98.7 N 77.6 N 120.3 N P 262 15534 112.6 N 157.8 N 108.8 N 109.2 N N 263 15535 83.4 N 82.3 N 77.5 N 95.9 N N 264 15536 191.6 Y 73.6 N 97.0 N 181.0 Y P 265 15537 71.6 N 90.2 N 48.5 N 54.7 N N 266 15538 86.6 N 81.1 N 60.3 N 61.0 N N 267 15539 8.8 N 13.4 N 5.2 N 6.3 N N 268 15540 79.7 N 82.8 N 74.6 N 110.2 N N 269 15541 165.9 Y 160.9 N 402.3 Y 327.1 Y P 270 15542 84.0 N 173.2 Y 67.5 N 57.6 N P 271 15543 21.4 N 90.1 N 35.2 N 30.1 N N 272 15544 33.8 N 78.7 N 43.6 N 43.7 N N 273 15546 52.1 N 105.3 N 69.6 N 92.2 N N 274 15547 54.3 N 88.6 N 131.4 N 40.7 N N 275 15548 15.1 N 34.6 N 30.2 N 19.0 N N 276 15549 57.2 N 19.2 N 31.9 N 21.8 N N 277 15550 69.1 N 95.3 N 46.7 N 37.9 N N 278 15551 67.0 N 50.6 N 74.2 N 73.1 N N 279 15552 31.7 N 62.0 N 59.1 N 115.6 N N 280 15553 45.1 N 61.2 N 64.5 N 69.7 N N 281 15554 32.1 N 21.6 N 26.6 N 22.0 N N 282 15555 68.9 N 29.9 N 79.3 N 35.3 N N 283 15556 62.7 N 159.2 N 52.5 N 35.9 N N 284 15557 50.8 N 29.5 N 52.6 N 20.5 N N 285 15558 126.8 N 73.2 N 85.8 N 105.9 N N 286 15559 46.0 N 49.0 N 46.1 N 26.0 N N

TABLE 9 Cox proportional hazard model for time to confirmed EDSS progression for classifier 1 with set of covariats. Parameter Standard Two-sided Hazard 95% Confidence Parameter Estimate Error Chi-Square p-value Ratio Interval Classifier 1 0.71694 0.27461 6.8162 0.0090 2.048 1.196-3.508 IFNB-1b −0.59253 0.25038 5.6004 0.0180 0.553 0.338-0.903 Steroid use 1^(st) event −0.29597 0.26421 1.2549 0.2626 0.744 0.443-1.248 >=9 T2 lesions 0.04083 0.29539 0.0191 0.8901 1.042 0.584-1.859 Multifocal onset −0.23473 0.24441 0.9224 0.3368 0.791 0.490-1.277 Age (per year) 0.03327 0.01661 4.0139 0.0451 1.034 1.001-1.068 Female 0.49537 0.31100 2.5371 0.1112 1.641 0.892-3.019 >=1 Gd-enh. lesions 0.79179 0.25799 9.4195 0.0021 2.207 1.331-3.660

TABLE 10 Estimated coefficient for the effect of classifier 1 in the proportional hazard regression models with additional covariates. Time-to-event Parameter Hazard ratio outcome Estimate SE Estimate 95% CI p-value Time to con- 0.7169 0.2746 2.048 1.196-3.508 0.0090 firmed EDSS progression CI = confidence interval; SE = standard error 

1. A method of identifying a subject with a clinically isolated syndrome (CIS) who will progress to clinically definitive multiple sclerosis (CDMS) within twenty-four months, the method comprising: providing a test sample from said subject; detecting in said test sample an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody; and comparing the levels of said antibodies in said test sample to a reference level of said antibodies, wherein a higher level of at least one of said antibodies compared to the reference level of said antibodies indicates that said subject is likely to progress to CDMS within twenty-four months.
 2. The method of claim 1, wherein a higher level of one of said antibodies in said test sample compared to the reference level of said antibodies indicates said subject is at risk of having a second neurological attack within thirty-six months.
 3. The method of claim 1, wherein a higher level of one of said antibodies in said test sample compared to the reference level of said antibodies indicates said subject is at risk of having a second neurological attack within forty-eight months.
 4. The method of claim 1, wherein said test sample is a biological fluid is whole blood, serum, or plasma.
 5. The method of claim 1, wherein said antibodies are IgM isotype antibodies.
 6. A method of identifying a subject with CIS who is likely to experience rapid progression of MS disease severity within twenty years, the method comprising: providing a test sample from a subject at the time of a CIS; detecting in said test sample an anti-Glc(α1,2)Glc(α) (GAGA2) antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) antibody, or an anti-Glc(α1,6)Glc(α) (GAGA6) antibody; comparing the sample level of each antibody in said test sample to a reference level of said antibody; and wherein a higher level of at least one of said antibodies compared to said reference level indicates that said subject is likely to progress greater than one unit in EDSS score within twenty years, said EDSS score being indicative of disease severity.
 7. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress at least two units in EDSS score within twenty years.
 8. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress at least three units in EDSS score within twenty years.
 9. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress greater than three units in EDSS score within twenty years.
 10. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress greater than six units in EDSS score within twenty years.
 11. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress greater than one unit in EDSS score within ten years
 12. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress greater than one unit in EDSS score within seven years.
 13. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress greater than one unit in EDSS score within five years.
 14. The method of claim 6, wherein a higher level of at least one of said antibodies indicates that said subject is likely to progress greater than one unit in EDSS score within four years.
 15. The method of claim 6, wherein said test sample is a biological fluid is whole blood, serum, or plasma.
 16. The method of claim 6, wherein said antibodies are IgM isotype antibodies.
 17. A method of identifying a subject with CIS who is likely to experience a slow progression of MS disease severity, the method comprising: providing a test sample from a subject at the time of a CIS; detecting in said test sample an anti-Glc(α1,2)Glc(α) (GAGA2) antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) antibody, and an anti-Glc(α1,6)Glc(α) (GAGA6) antibody; comparing the sample level of each antibody in said test sample to a reference level of said antibody; and wherein a equal or lower level of each of said antibodies indicates that said subject is likely to progress slowly in EDSS score within twenty years, said EDSS score being indicative of disease severity.
 18. The method of claim 17, wherein an equal or lower level of each of said antibodies indicates that said subject is likely to not progress greater than one unit in EDSS score within five years.
 19. The method of claim 17, wherein said test sample is a biological fluid is whole blood, serum, or plasma.
 20. The method of claim 17, wherein said antibodies are IgM isotype antibodies.
 21. A method of identifying a subject who will develop breakthrough MS, the method comprising: providing a test sample from said subject; detecting in said test sample an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody; and comparing the levels of said antibodies in said test sample to a reference level of said antibodies, wherein a higher level of at least one of said antibodies compared to the reference level of said antibodies indicates that said subject is likely to develop breakthrough MS.
 22. The method of claim 21, wherein a higher level of at least one of said antibodies indicates that said subject is likely to develop breakthrough MS within five years.
 23. The method of claim 21, wherein said test sample is a biological fluid is whole blood, serum, or plasma.
 24. The method of claim 21, wherein said antibodies are IgM isotype antibodies.
 25. A computer-readable medium having computer-executable instructions for performing a method comprising: storing at least one first variable associated with the level of at least one antibody in a test sample of a CIS patient; storing at least one second variable associated with at least one reference level; calculating the patient's risk factor for conversion to clinically definite multiple sclerosis (CDMS) as a function of at least the first and second variables; and outputting the risk factor.
 26. The computer-readable medium of claim 25, wherein the at least one first variable corresponds to levels of an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody in the test sample.
 27. The computer-readable medium of claim 25, wherein the at least one first variable corresponds to at least one level of antibody in the test sample selected from the group consisting of: an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, and an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody.
 28. A computer-readable medium having computer-executable instructions for performing a method comprising: storing at least one first variable associated with the level of at least one antibody in a test sample of a CIS patient; storing at least one second variable associated with at least one reference level; calculating the patient's risk factor for progressing in EDSS score as a function of at least the first and second variables; and outputting the risk factor.
 29. The computer-readable medium of claim 28, wherein the at least one first variable corresponds to levels of an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody in the test sample.
 30. The computer-readable medium of claim 28, wherein the at least one first variable corresponds to at least one level of antibody in the test sample selected from the group consisting of an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, and an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody.
 31. A computer-readable medium having computer-executable instructions for performing a method comprising: storing at least one first variable associated with the level of at least one antibody in a test sample of a patient; storing at least one second variable associated with at least one reference level; calculating the patient's risk factor for developing breakthrough MS as a function of at least the first and second variables; and outputting the risk factor.
 32. The computer-readable medium of claim 31, wherein the at least one first variable corresponds to levels of an anti-Glc (α 1,2) Glc (α) antibody, an anti-Glc (α 1,3) Glc (α) antibody, an anti-Glc (α 1,4) Glc (α) antibody, and an anti-Glc (α 1,6) Glc (α) antibody in the test sample.
 33. The computer-readable medium of claim 31, wherein the at least one first variable corresponds to at least one level of antibody in the test sample selected from the group consisting of: an anti-Glc(α1,2)Glc(α) (GAGA2) IgM isotype antibody, an anti-Glc(α1,3)Glc(α) (GAGA3) IgM isotype antibody, an anti-Glc(α1,4)Glc(α) (GAGA4) IgM isotype antibody, and an anti-Glc(α1,6)Glc(α) (GAGA6) IgM isotype antibody. 